Analysis of Role of Dams on Recorded Dry Season Malaria Incidences in Kasungu
Clinton Nkolokosa
Last edited 22 July 2021
This geospatial statistical model uses routinely collected malaria case data, population data and remotely sensed data, such as open and vegetated water bodies, to estimate population living around open water bodies, expected malaria cases, and standardised morbidity ratio (SMR) of malaria. And ultimately, quantify the association between proximity to larval habitat and malaria risk in health facility catchment areas in Kasungu. The SMR compares the risk of morbidity in a population of interest with that of a standard population. In this case, our interest is to find out whether the number of dry season malaria cases in each catchment area are greater than we would expect given the malaria rate for the entire Kasungu district.
We do this by comparing what we observe (O) with what we would expect (E) if the risk of malaria was equal throughout Kasungu. The SMR statistical notation of catchment i can be written as follows: \[SMR_i = \frac{O_i}{E_i}\]
Buffers around waterbodies are created and then combined with population data in raster format to estimate the proprtion of catcment population living within 1km, 2km and 3km of water bodies. Subsequently, the observed malaria cases are modeled using Poisson regression to find out if living within various distances from water bodies is causing variability in malaria risk in Kasungu district. We hypothesize that the risk of being a case in a catchment is dependent on proximity to water bodies. The data used spans from 2017 to 2020 and was derived from digitized DHIS2 malaria records, accessibility mapping, aggregated population geospatial layer and TropWet tool in Google Earth Engine.
1 Load packages
Loading the R packages that will be used to read in, view, transform and model the malaria cases and spatial datasets.
suppressPackageStartupMessages({
library(SpatialEpi)
library(spdep)
library(spaMM)
library(popEpi)
library(Epi)
library(epitools) # compute confidence intervals of malaria data
library(tidyverse)
library(caret) # easily compute cross-validation methods to test model perfomance
library(stats)
library(performance) # check linear model performance
library(see) # to plot model assumptions
library(caTools) # splitting data into training and test data sets
library(qqplotr)
library(automap) # auto krigging
library(DescTools) # tools for descriptive statistics e.g., pseudo R-squared
library(gtsummary) # easily display regression model outputs, such as P value, CI
library(ggpubr)
library(plotly)
library(lubridate)
library(knitr)
library(raster)
library(rgdal)
library(rgeos)
library(sf)
library(sp)
library(tmap)
library(spdep)
library(maptools)
library(gridExtra)
library(ggsci)
library(grid)
library(exactextractr)
library(DataExplorer)
library(mapview)
library(kableExtra) # create interactive tables
library(gt) # create beautiful HTML tables
library(imputeTS) # easily remove NAs
`%>%` <- magrittr::`%>%`
})1.1 Tell R where the data is
file.path(getwd(),"data")## [1] "C:/Users/cnkolokosa/Documents/R/upscaled_2021_updated_May/upscaled_2021/data"here::here()## [1] "C:/Users/cnkolokosa/Documents/R/upscaled_2021_updated_May/upscaled_2021"2 Load datasets
The total dry season malaria cases recorded at health-care facilities in Kasungu from 2017 to 2019 are contained in the KasunguData.csv sourced from https://dhis2.health.gov.mw/. The kasungu_facility_catchments_2004.shp shapefile also contains the population and health information within each health-facility catchment area in Kasungu district.
The aggregated population raster layers for Malawi e.g.,ku_pop_2017_1km_aggregated.tif were downloaded from the Open Spatial and Demographic and Data Research website: https://www.worldpop.org/geodata/country?iso3=MWI. These layers estimate total number of people per grid-cell. The units are number of people per pixel with country totals adjusted to match the corresponding official United Nations population estimates. The datasets were downloaded in Geotiff at a resolution of 1km and are projected in Geographic Coordinate System, WGS84.
The kasungu_water.shpand water_bodies layers contain open and vegetated waterbodies polygons, detected using the Tropical Wetland Unmixing Tool (TropWet). TropWet is a Google Earth Engine hosted toolbox that uses the Landsat archive to map tropical wetlands and can be accessed through: https://www.aber.ac.uk/en/dges/research/earth-observation-laboratory/research/tropwet/
# Kasungu dry season malaria data
dry_season_malaria_2017_2020 <- read.csv(here::here("data/dry_season_malaria_2017_2020.csv"),
stringsAsFactors = FALSE)
# Kasungu monthly NMCP confirmed malaria cases
monthly_malaria_2017_2021 <- read.csv(here::here("data/Kasungu monthly malaria 2017-2021.csv"),
stringsAsFactors = FALSE)
monthly_malaria_2017_2021$date <- lubridate::ym(monthly_malaria_2017_2021$periodid)
# Kasungu district boundary shapefile
kasungu_district <- sf::st_read(here::here("data", "kasungu_district.shp"))## Reading layer `kasungu_district' from data source
## `C:\Users\cnkolokosa\Documents\R\upscaled_2021_updated_May\upscaled_2021\data\kasungu_district.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 1 feature and 5 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 491272.7 ymin: 8494349 xmax: 609044.2 ymax: 8632164
## Projected CRS: WGS 84 / UTM zone 36S# Kasungu health facility catchments generated from accessibility mapping
malire_new <- sf::st_read(here::here("data", "zipatala_catchment_areas.shp")) |>
sf::st_transform(32736) # reproject to WGS UTM Zone 36 South## Reading layer `zipatala_catchment_areas' from data source
## `C:\Users\cnkolokosa\Documents\R\upscaled_2021_updated_May\upscaled_2021\data\zipatala_catchment_areas.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 26 features and 2 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 32.925 ymin: -13.61667 xmax: 34.00833 ymax: -12.375
## Geodetic CRS: WGS 84# Kasungu population raster layer
kasungu_population_2017 <- raster(here::here("data", "ku_pop_2017_1km_aggregated.tif"))
kasungu_population_2018 <- raster(here::here("data", "ku_pop_2018_1km_aggregated.tif"))
kasungu_population_2019 <- raster(here::here("data", "ku_pop_2019_1km_aggregated.tif"))
kasungu_population_2020 <- raster(here::here("data", "ku_pop_2020_1km_aggregated.tif"))
# Read in waterbodies polygons
dryseason_waterbodies_2017 <- sf::st_read(here::here("data", "water_bodies_2017.shp"))## Reading layer `water_bodies_2017' from data source
## `C:\Users\cnkolokosa\Documents\R\upscaled_2021_updated_May\upscaled_2021\data\water_bodies_2017.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 168 features and 1 field
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 514497 ymin: 8495941 xmax: 603149.8 ymax: 8620169
## Projected CRS: WGS 84 / UTM zone 36Sdryseason_waterbodies_2018 <- sf::st_read(here::here("data", "kasungu_2018_water.shp"))## Reading layer `kasungu_2018_water' from data source
## `C:\Users\cnkolokosa\Documents\R\upscaled_2021_updated_May\upscaled_2021\data\kasungu_2018_water.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 1105 features and 1 field
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 496807.6 ymin: 8494693 xmax: 607913.8 ymax: 8607747
## Projected CRS: WGS 84 / UTM zone 36Sdryseason_waterbodies_2019 <- sf::st_read(here::here("data", "kasungu_2019_water.shp"))## Reading layer `kasungu_2019_water' from data source
## `C:\Users\cnkolokosa\Documents\R\upscaled_2021_updated_May\upscaled_2021\data\kasungu_2019_water.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 1941 features and 1 field
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 494197.2 ymin: 8494693 xmax: 607913.8 ymax: 8617573
## Projected CRS: WGS 84 / UTM zone 36Sdryseason_waterbodies_2020 <- sf::st_read(here::here("data", "water_bodies_2020.shp"))## Reading layer `water_bodies_2020' from data source
## `C:\Users\cnkolokosa\Documents\R\upscaled_2021_updated_May\upscaled_2021\data\water_bodies_2020.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 266 features and 1 field
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 508985.6 ymin: 8495793 xmax: 585761.1 ymax: 8620169
## Projected CRS: WGS 84 / UTM zone 36S# Add a row ID to water bodies polygons
dryseason_waterbodies_2017$ID <- 1:nrow(dryseason_waterbodies_2017)
dryseason_waterbodies_2018$ID <- 1:nrow(dryseason_waterbodies_2018)
dryseason_waterbodies_2019$ID <- 1:nrow(dryseason_waterbodies_2019)
dryseason_waterbodies_2020$ID <- 1:nrow(dryseason_waterbodies_2020)2.1 View the dry season malaria case data
Lets have a closer look at the malaria dataset. We observe that Kasungu district has 30 health facilities classified as dispensary, health centre, district hospital and rural hospital, and the highest malaria cases were recorded at Kasungu District Hospital.
dry_season_malaria_2017_2020 |>
summary()## X rowID Names dr_2017
## Min. : 1.00 Min. : 1.00 Length:36 Min. : 0.0
## 1st Qu.: 9.75 1st Qu.: 9.75 Class :character 1st Qu.: 918.8
## Median :18.50 Median :18.50 Mode :character Median : 1505.0
## Mean :18.50 Mean :18.50 Mean : 1818.1
## 3rd Qu.:27.25 3rd Qu.:27.25 3rd Qu.: 2150.0
## Max. :36.00 Max. :36.00 Max. :11976.0
##
## dr_2018 dr_2019 dr_2020 LONGITU
## Min. : 0 Min. : 0 Min. : 0 Min. :33.18
## 1st Qu.:1180 1st Qu.: 1152 1st Qu.: 1650 1st Qu.:33.38
## Median :1706 Median : 1569 Median : 2698 Median :33.50
## Mean :2093 Mean : 2039 Mean : 3404 Mean :33.52
## 3rd Qu.:2754 3rd Qu.: 2512 3rd Qu.: 4722 3rd Qu.:33.68
## Max. :9820 Max. :11399 Max. :16435 Max. :33.87
## NA's :6
## LATITUD
## Min. :-13.57
## 1st Qu.:-13.25
## Median :-12.98
## Mean :-12.99
## 3rd Qu.:-12.79
## Max. :-12.42
## NA's :6# Plotly bar chart -------------------------------------------------------------
bar_chart <- dry_season_malaria_2017_2020 |>
dplyr::filter(Names != "K2 Taso Clinic", # Have missing malaria records
Names != "Kalikeni Private Clinic",
Names != "Kakwale Health Centre",
Names != "St Andrews Community Hospital",
Names != "St. Faith Health Centre",
Names != "Chambwe Health Centre") |>
plotly::plot_ly(y = ~Names,
x = ~dr_2017,
type = "bar",
orientation = 'h',
name = "2017") |>
plotly::add_trace(x = ~ dr_2018,
name = "2018") |>
plotly::add_trace(x = ~ dr_2019,
name = "2019") |>
plotly::add_trace(x = ~ dr_2020,
name = "2020") |>
plotly::layout(xaxis = list(title = "Total malaria cases"),
yaxis = list(title = " "),
hovermode = "compare",
margin = list(b = 10,
t = 10,
pad = 2))
bar_chartFig.1 The total malaria cases recorded at each health-care facility in Kasungu district
# Pivot longer -----------------------------------------------------------------
# dry_season_malaria_longer <- dry_season_malaria_2017_2020 |>
# dplyr::filter(Names != "K2 Taso Clinic",
# Names != "Kalikeni Private Clinic",
# Names != "Kakwale Health Centre",
# Names != "St Andrews Community Hospital",
# Names != "St. Faith Health Centre",
# Names != "Chambwe Health Centre") |>
# dplyr::rename(`2017` = dr_2017,
# `2018` = dr_2018,
# `2019` = dr_2019,
# `2020` = dr_2020) |>
# tidyr::pivot_longer(cols = `2017`:`2020`,
# names_to = 'year',
# values_to = 'malaria_cases')
#
# ggplot2::ggplot(dry_season_malaria_longer,
# aes(x = malaria_cases,
# y = Names,
# fill = year))+
# ggplot2::geom_bar(stat ='identity',
# position = "dodge")+
# ggplot2::labs(x = "Dry season malaria cases",
# y = " ")+
# ggplot2::theme_classic()+
# ggsci::scale_fill_jama()2.1.1 Monthly variation in malaria cases
ggplot2::ggplot(monthly_malaria_2017_2021) +
aes(x = date,
y = NMCP) +
geom_line(size = 0.6,
colour = "#112446") +
scale_y_continuous(trans = "log2") +
labs(x = "Year",
y = "Confirmed malaria cases") +
theme_classic()
Fig.2: Seasonal variation in confirmed malaria cases
3 Kasungu health-care facilities and their catchment areas
Heath facility catchment area is the area from which a health facility attracts patients. Since the available official catchment areas are oudated and no recent spatial data about the catchment areas is available, new health facility catchments polygon were generated from generic accessibility mapping script adapted from https://malariaatlas.org/wp-content/uploads/accessibility/R_generic_accessibilty_mapping_script.r The script requires two user supplied datasets: the 2015 friction surface, which is available here: http://www.map.ox.ac.uk/accessibility_to_cities/, and a user-supplied .csv of points dry_season_malaria_2017_2020. The accumulated cost algorithm accCost and r.Cost algorithm in QGIS were run to make the final output map of new health facility catchment boundaries.
# Using the complete.cases() function to select health centres with complete
# longitude and latitude coordinates.
zipatala <- dry_season_malaria_2017_2020[complete.cases(dry_season_malaria_2017_2020),]
# Aggregate health facilities close to each other:
# a) Kasalika Health Centre and Kasungu District Hospital,
# b) Bua and Mziza Health Centres, and
# c) Kaluluma and Nkhamenya Rural Hospitals
# d) Anchor and Santhe Health Centres in order to
# generate catchment areas that are geographically correct
zipatala$dr_2017[which(
zipatala$Names == "Kasungu District Hospital")] <- zipatala$dr_2017[which(
zipatala$Names == "Kasungu District Hospital")] + zipatala$dr_2017[which(
zipatala$Names == "Kasalika Health Centre")]
zipatala$dr_2018[which(
zipatala$Names == "Kasungu District Hospital")] <- zipatala$dr_2018[which(
zipatala$Names == "Kasungu District Hospital")] + zipatala$dr_2018[which(
zipatala$Names == "Kasalika Health Centre")]
zipatala$dr_2019[which(
zipatala$Names == "Kasungu District Hospital")] <- zipatala$dr_2019[which(
zipatala$Names == "Kasungu District Hospital")] + zipatala$dr_2019[which(
zipatala$Names == "Kasalika Health Centre")]
zipatala$dr_2020[which(
zipatala$Names == "Kasungu District Hospital")] <- zipatala$dr_2020[which(
zipatala$Names == "Kasungu District Hospital")] + zipatala$dr_2020[which(
zipatala$Names == "Kasalika Health Centre")]
zipatala$dr_2017[which(
zipatala$Names == "Nkhamenya Rural Hospital")] <- zipatala$dr_2017[which(
zipatala$Names == "Nkhamenya Rural Hospital")] + zipatala$dr_2017[which(
zipatala$Names == "Kaluluma Rural Hospital")]
zipatala$dr_2018[which(
zipatala$Names == "Nkhamenya Rural Hospital")] <- zipatala$dr_2018[which(
zipatala$Names == "Nkhamenya Rural Hospital")] +zipatala$dr_2018[which(
zipatala$Names == "Kaluluma Rural Hospital")]
zipatala$dr_2019[which(
zipatala$Names == "Nkhamenya Rural Hospital")] <- zipatala$dr_2019[which(
zipatala$Names == "Nkhamenya Rural Hospital")] + zipatala$dr_2019[which(
zipatala$Names == "Kaluluma Rural Hospital")]
zipatala$dr_2020[which(
zipatala$Names == "Nkhamenya Rural Hospital")] <- zipatala$dr_2020[which(
zipatala$Names == "Nkhamenya Rural Hospital")] + zipatala$dr_2020[which(
zipatala$Names == "Kaluluma Rural Hospital")]
zipatala$dr_2017[which(
zipatala$Names == "Mziza Health Centre")] <- zipatala$dr_2017[which(
zipatala$Names == "Mziza Health Centre")] + zipatala$dr_2017[which(
zipatala$Names == "Bua Health Centre")]
zipatala$dr_2018[which(
zipatala$Names == "Mziza Health Centre")] <- zipatala$dr_2018[which(
zipatala$Names == "Mziza Health Centre")] + zipatala$dr_2018[which(
zipatala$Names == "Bua Health Centre")]
zipatala$dr_2019[which(
zipatala$Names == "Mziza Health Centre")] <- zipatala$dr_2019[which(
zipatala$Names == "Mziza Health Centre")] + zipatala$dr_2019[which(
zipatala$Names == "Bua Health Centre")]
zipatala$dr_2020[which(
zipatala$Names == "Mziza Health Centre")] <- zipatala$dr_2020[which(
zipatala$Names == "Mziza Health Centre")] + zipatala$dr_2020[which(
zipatala$Names == "Bua Health Centre")]
zipatala$dr_2017[which(
zipatala$Names == "Santhe Health Centre")] <- zipatala$dr_2017[which(
zipatala$Names == "Santhe Health Centre")] + zipatala$dr_2017[which(
zipatala$Names == "Anchor Farm")]
zipatala$dr_2018[which(
zipatala$Names == "Santhe Health Centre")] <- zipatala$dr_2018[which(
zipatala$Names == "Santhe Health Centre")] + zipatala$dr_2018[which(
zipatala$Names == "Anchor Farm")]
zipatala$dr_2019[which(
zipatala$Names == "Santhe Health Centre")] <- zipatala$dr_2019[which(
zipatala$Names == "Santhe Health Centre")] + zipatala$dr_2019[which(
zipatala$Names == "Anchor Farm")]
zipatala$dr_2020[which(
zipatala$Names == "Santhe Health Centre")] <- zipatala$dr_2020[which(
zipatala$Names == "Santhe Health Centre")] + zipatala$dr_2020[which(
zipatala$Names == "Anchor Farm")]
# Drop out the other health facilities
zipatala_aggregated <- zipatala |>
dplyr::filter(Names != "Kasalika Health Centre",
Names != "Bua Health Centre",
Names != "Kaluluma Rural Hospital",
Names != "Anchor Farm")
# Save csv file
# write.csv(zipatala_aggregated, "data/zipatala_aggregated.csv")
# Convert to csv to sf object
zipatala_aggregated_sf <- sf::st_as_sf(zipatala_aggregated,
coords = c("LONGITU", "LATITUD"),
crs = 4326, agr = "constant")
# st_write(zipatala_aggregated_sf, "data/zipatala_combined.shp")3.1 View location of the health facilities in the new catchment areas
# Plot map
tm_shape(malire_new)+
tm_polygons()+
tm_shape(zipatala_aggregated_sf)+
tm_dots(size = .3,
col = "blue",
alpha = 0.5)+
tm_text("Names",
size = .3,
just = "top",
col = "black",
remove.overlap = TRUE)+
tm_layout(frame = FALSE,
title = "New Kasungu health facility \n catchment boundaries",
title.size = .8,
title.position = c("left", "top"))+
tm_compass(position=c("right", "top"))+
tm_scale_bar(breaks = c(0, 10, 20),
text.size = .5)
Fig 3. Kasungu health-care facilities and catchment areas
3.2 Kasungu district estimated population per grid-cell
# Take a glimpse at the WorldPop raster layers
kasungu_population_2017## class : RasterLayer
## dimensions : 150, 128, 19200 (nrow, ncol, ncell)
## resolution : 920.0898, 918.7667 (x, y)
## extent : 491272.7, 609044.2, 8494349, 8632164 (xmin, xmax, ymin, ymax)
## crs : +proj=utm +zone=36 +south +datum=WGS84 +units=m +no_defs
## source : ku_pop_2017_1km_aggregated.tif
## names : ku_pop_2017_1km_aggregatedkasungu_population_2018## class : RasterLayer
## dimensions : 150, 128, 19200 (nrow, ncol, ncell)
## resolution : 920.0898, 918.7667 (x, y)
## extent : 491272.7, 609044.2, 8494349, 8632164 (xmin, xmax, ymin, ymax)
## crs : +proj=utm +zone=36 +south +datum=WGS84 +units=m +no_defs
## source : ku_pop_2018_1km_aggregated.tif
## names : ku_pop_2018_1km_aggregated
## values : 0, 6253.557 (min, max)kasungu_population_2019## class : RasterLayer
## dimensions : 150, 128, 19200 (nrow, ncol, ncell)
## resolution : 920.0898, 918.7667 (x, y)
## extent : 491272.7, 609044.2, 8494349, 8632164 (xmin, xmax, ymin, ymax)
## crs : +proj=utm +zone=36 +south +datum=WGS84 +units=m +no_defs
## source : ku_pop_2019_1km_aggregated.tif
## names : ku_pop_2019_1km_aggregated
## values : 0, 6483.727 (min, max)kasungu_population_2020## class : RasterLayer
## dimensions : 150, 128, 19200 (nrow, ncol, ncell)
## resolution : 920.0898, 918.7667 (x, y)
## extent : 491272.7, 609044.2, 8494349, 8632164 (xmin, xmax, ymin, ymax)
## crs : +proj=utm +zone=36 +south +datum=WGS84 +units=m +no_defs
## source : ku_pop_2020_1km_aggregated.tif
## names : ku_pop_2020_1km_aggregated
## values : 0, 7949.033 (min, max)# Helper function to create a raster population map
create.population.map <- function(population.raster, title){
# raster population map
# arguments:
# population.raster: aggregated population raster layer from WorldPop
# legend.title: legend title
# returns:
# a tmap-element (plots a map)
# set to interactive view
# tmap::tmap_mode("view")
# plot map
tmap::tm_shape(population.raster)+
tmap::tm_raster(palette = "-viridis",
title = title,
breaks = c(0,100,200,400,600,800,1000,2000,4000,6000,8000))+
tmap::tm_layout(legend.position = c("right", "bottom"),
frame = FALSE)+
tmap::tm_scale_bar(position = c("left", "bottom"))
}
# Set to static map
tmap_mode("plot")
estimated_pop_2017 <- create.population.map(kasungu_population_2017, title = "2017 Population")
estimated_pop_2018 <- create.population.map(kasungu_population_2018, title = "2018 Population")
estimated_pop_2019 <- create.population.map(kasungu_population_2019, title = "2019 Population")
estimated_pop_2020 <- create.population.map(kasungu_population_2020, title = "2020 Population")
# Layout the maps
tmap::tmap_arrange(estimated_pop_2017, estimated_pop_2018,
estimated_pop_2019, estimated_pop_2020, nrow = 2) 
Fig.4 Estimated total number of people per 1km grid-cell
4 Assign dry season malaria cases and population density to new health facility catchments
The WorldPop aggregated population e.g. kasungu_population_2017.tif, and DHIS2 malaria dry_season_malaria_2017_2020 datasets are assigned to the new health facility catchments.
# Helper function that assigns malaria data from health facilities to their catchments areas -------
assign.malaria.data <- function(catchment_boundary, malaria_data){
# arguments:
# catchment_boundary: sf polygon object of new catchment boundaries
# malaria_data: sf point object with a data frame containing the dry season malaria cases
# returns:
# catchments_malaria_sf: sf polygon object with a data frame containing dry season malaria cases
# Convert sf objects to spatial
catchment_shp <- as(catchment_boundary, "Spatial")
malaria_shp <- as(malaria_data, "Spatial")
# Match CRS
malaria_shp <- spTransform(malaria_shp, crs(catchment_shp))
# Overlay aggregated health facility points and extract 2017 - 2020 malaria cases
# Using 'point.in.poly' to return a point spatial object, in this case location of health facilities
# and estimated population instead of sp::over function, which simply returns
# a data frame, with the same no. rows.
# Argument 'sp = TRUE' returns an sp class object, else returns sf class object
# Joining the malaria and population dataset using only 'merge' function can't work due to
# non-unique columns and differences in row numbers
hospitals_in_catchment <- spatialEco::point.in.poly(malaria_shp, catchment_shp, sp = TRUE)
# Add the extracted ID, health facility names and dry season malaria cases to
# the health facility catchments (hfc)
hfc_malaria_shp <- merge(catchment_shp, hospitals_in_catchment, by.x = "DN", by.y = "rowID")
# Convert the shapefile containing malaria data to sf-object
hfc_malaria_sf <- sf::st_as_sf(hfc_malaria_shp)
# Tidy the data by dropping columns not needed
catchment_malaria <- hfc_malaria_sf |>
dplyr::select(-c(coords.x1, coords.x2))
return(out = catchment_malaria)
}
# Invoking the function ------------------------------------------------------------------
malaria_by_catchment <- assign.malaria.data(malire_new, zipatala_aggregated_sf)5 Assign population data to the health catchment areas
# Helper unction to extract population from WorldPop raster file and assign ---------------------------
# the values to the new catchments.
extract.pop.values <- function(kasungu_pop_raster, catchments){
# function to extract population from raster file and assign the population to catchments
# arguments:
# kasungu_pop_raster: population raster file clipped to Kasungu district
# catchments: shapefile containing the polygons that we wish to use as boundaries
# returns:
# catchments_malaria_pop_sf: sf polygon object containing malaria and population data
# convert from sf to sp
catchments_sp <- as(catchments, "Spatial")
# Match extent i.e projection
catchments_sp <- spTransform(catchments_sp, proj4string(kasungu_pop_raster))
# Crop and mask the population raster to exclude Kasungu National Park
pop_raster_clip <- raster::crop(kasungu_pop_raster, extent(catchments_sp)) |>
raster::mask(catchments_sp)
# pop_raster_clip <- raster::mask(raster::crop(kasungu_pop_raster, extent(catchments_sp)), catchments_sp)
# Extracting zonal statistics from a population raster layer.
# The population raster is a continuous gridded surface layer that has an
# estimated population density value to every square in their grid.
# The population values are then summed and apportioned to the catchment polygons
# catchments_malaria_pop <- catchments %>%
# dplyr::mutate(pop = round(raster::extract(pop_raster_clip, catchments, fun = sum, na.rm = TRUE)))
pop_by_catchment <- round(raster::extract(pop_raster_clip, catchments, fun = sum, na.rm = TRUE))
pop_by_catchment_df <- pop_by_catchment %>%
# apply unlist to the lists to have vectors as the list elements
lapply(unlist) %>%
# convert vectors to data.frames
lapply(as_tibble) %>%
# combine the list of data.frames
bind_rows(., .id = "rowID") %>%
# rename the value variable
dplyr::rename(pop = value)
# Add row ID to column to catchment layer
catchments$rowID <- 1:nrow(catchments)
# Merge catchment areas and population data
pop_by_catchments <- merge(catchments, pop_by_catchment_df, by = "rowID")
# Cleaning 'Inf' values
pop_by_catchments |>
dplyr::mutate_if(is.numeric, list(~na_if(., Inf))) |>
dplyr::mutate_if(is.numeric, list(~na_if(., -Inf)))
return(out = pop_by_catchments)
}
# Invoking the function -------------------------------------------------------------------------
malaria_pop_by_catchment_2017 <- extract.pop.values(kasungu_population_2017, malaria_by_catchment)
malaria_pop_by_catchment_2018 <- extract.pop.values(kasungu_population_2018, malaria_by_catchment)
malaria_pop_by_catchment_2019 <- extract.pop.values(kasungu_population_2019, malaria_by_catchment)
malaria_pop_by_catchment_2020 <- extract.pop.values(kasungu_population_2020, malaria_by_catchment)5.1 View population by catchment maps
Estimated total number of people within health facility catchment areas.
# max(malaria_pop_by_catchment_2017$pop)
# [1] 143490
# max(malaria_pop_by_catchment_2018$pop)
# [1] 147175
# max(malaria_pop_by_catchment_2019$pop)
# [1] 151079
# max(malaria_pop_by_catchment_2020$pop)
# [1] 185282
# Custom function to create maps of estimated population by catchment areas --------------------------
create.population.map <- function(catchment.area,
variable = "pop",
title,
legend.title = "Estimated \n population"){
# estimated population map
# catchment.area: estimated population layer from nachulu function
# variable: variable name (as character, in qoutes)
# title: map title in quotes
# legend.title: legend title in qoutes
# returns:
# a tmap-element (plots a map)
tm_shape(catchment.area)+
tm_fill(col = variable,
breaks = c(0, 13000, 19000, 27000, 35000, 70000, 140000, 200000),
palette = "YlOrBr",
title = legend.title)+
tm_borders(col = "grey",
lwd = 0.4)+
tm_layout(legend.position = c(0.75, "bottom"),
legend.text.size = 0.6,
legend.title.size = 0.8,
frame = FALSE)+
tm_credits(title,
position = c(0.3, 0.8),
size = 1)
}
# Invoking the function --------------------------------------------------------------------------
pop_by_catchment_2017 <- create.population.map(malaria_pop_by_catchment_2017, title = "2017")
pop_by_catchment_2018 <- create.population.map(malaria_pop_by_catchment_2018, title = "2018")
pop_by_catchment_2019 <- create.population.map(malaria_pop_by_catchment_2019, title = "2019")
pop_by_catchment_2020 <- create.population.map(malaria_pop_by_catchment_2020, title = "2020")
tmap::tmap_arrange(pop_by_catchment_2017, pop_by_catchment_2018,
pop_by_catchment_2019, pop_by_catchment_2020, ncol = 2)
Fig. 5: Estimated population by health facility catchment areas
5.2 Population density by catchment
# Helper function to calculate population density by catchment -----------------
calculate.population.density <- function(pop.data){
# Convert to spatial object
pop.sp <- as(pop.data, "Spatial")
# Calculate area of catchment polygon in square kilometres
pop.sp$area_sqkm <- round(rgeos::gArea(pop.sp, byid = TRUE) / (1000 * 1000))
# Calculate population density
pop.sp$pop_density <- round(pop.sp$pop / pop.sp$area_sqkm)
# Convert back to sf object
pop.sf <- sf::st_as_sf(pop.sp)
return(pop.sf)
}
# Invoking function ------------------------------------------------------------
pop_density_2017 <- calculate.population.density(malaria_pop_by_catchment_2017)
pop_density_2018 <- calculate.population.density(malaria_pop_by_catchment_2018)
pop_density_2019 <- calculate.population.density(malaria_pop_by_catchment_2019)
pop_density_2020 <- calculate.population.density(malaria_pop_by_catchment_2020)
# Helper function to create population density maps ----------------------------
create.pop.density.map <- function(pop.density.data,
variable = "pop_density",
title = NA,
legend.title = "Population \ndensity/km^2"){
tm_shape(pop.density.data)+
tm_fill(col = variable,
breaks = c(0, 50, 100, 150, 200, 250, 300, 350),
palette = "-magma",
title = legend.title)+
tm_borders()+
tm_layout(legend.position = c(0.75, "bottom"),
legend.text.size = 0.6,
legend.title.size = 0.8,
frame = FALSE)+
tm_credits(title,
position = c(0.3, 0.8),
size = 1)
}
# Invoking function ------------------------------------------------------------
pop_density_2017_map <- create.pop.density.map(pop_density_2017, title = "2017")
pop_density_2018_map <- create.pop.density.map(pop_density_2018, title = "2018")
pop_density_2019_map <- create.pop.density.map(pop_density_2019, title = "2019")
pop_density_2020_map <- create.pop.density.map(pop_density_2020, title = "2020")
# Layout maps
tmap::tmap_arrange(pop_density_2017_map, pop_density_2018_map,
pop_density_2019_map, pop_density_2020_map, ncol = 2)
Fig. 6: Estimated population density by health facility catchment areas
6 Calculate the expected number of cases for each catchment area
The expected number of dry season malaria cases in catchment i are calculated as the observed risk (r) of malaria i.e. the total number of malaria cases in Kasungu district divided by the total population of the district, multiplied by the number of people in the catchment area: \[E_i = \frac{\sum_i O_i}{\sum_i N_i}\times N_i\]
The expected number of dry season malaria cases are calculated under the assumption that there is no spatial variation in risk, i.e., no difference in infection rates between the catchment areas.
# Compute and print the overall incidence of dry season malaria cases
overall_malaria_incidence_rate_2017 <- round(sum(
malaria_pop_by_catchment_2017$dr_2017) / sum(
malaria_pop_by_catchment_2017$pop), 2)
overall_malaria_incidence_rate_2017## [1] 0.08overall_malaria_incidence_rate_2018 <- round(sum(
malaria_pop_by_catchment_2018$dr_2018) / sum(
malaria_pop_by_catchment_2018$pop), 2)
overall_malaria_incidence_rate_2018## [1] 0.09overall_malaria_incidence_rate_2019 <- round(sum(
malaria_pop_by_catchment_2019$dr_2019) / sum(
malaria_pop_by_catchment_2019$pop), 2)
overall_malaria_incidence_rate_2019## [1] 0.09overall_malaria_incidence_rate_2020 <- round(sum(
malaria_pop_by_catchment_2020$dr_2020) / sum(
malaria_pop_by_catchment_2020$pop), 2)
overall_malaria_incidence_rate_2020## [1] 0.12# Calculate expected malaria cases ------------------------------------------------
expected_malaria_2017 <- malaria_pop_by_catchment_2017 |>
dplyr::rename(
observed_2017 = dr_2017,
pop_2017 = pop) |>
dplyr::mutate(
expected_2017 = round(sum(observed_2017)/sum(pop_2017, na.rm = TRUE)*pop_2017))
expected_malaria_2018 <- malaria_pop_by_catchment_2018 |>
dplyr::rename(
observed_2018 = dr_2018,
pop_2018 = pop) |>
dplyr::mutate(
expected_2018 = round(sum(observed_2018)/sum(pop_2018, na.rm = TRUE)*pop_2018))
expected_malaria_2019 <- malaria_pop_by_catchment_2019 |>
dplyr::rename(
observed_2019 = dr_2019,
pop_2019 = pop) |>
dplyr::mutate(
expected_2019 = round(sum(observed_2019)/sum(pop_2019, na.rm = TRUE)*pop_2019))
expected_malaria_2020 <- malaria_pop_by_catchment_2020 |>
dplyr::rename(
observed_2020 = dr_2020,
pop_2020 = pop) |>
dplyr::mutate(
expected_2020 = round(sum(observed_2020)/sum(pop_2020, na.rm = TRUE)*pop_2020))7 Calculate the Standardised Morbidity Ratio of malaria incidences for each catchment area
The SMR compares the risk of morbidity in a population of interest with that of a standard population. In this case, our interest is to find out whether the number of dry season malaria cases in each catchment area are greater than we would expect given the malaria rate for the entire Kasungu district.
We do this by comparing what we observe (O) with what we would expect (E) if the risk of malaria was equal throughout Kasungu. The SMR of catchment i can be calculated as follows: \[SMR_i = \frac{O_i}{E_i}\]
SMRs above 1 represent high rates of dry season malaria and SMRs below 1, viceversa.
# Calculate the ratio of observed to expected (SMR) ----------------------------
SMR_2017 <- expected_malaria_2017 |>
dplyr::mutate(SMR = round(observed_2017/expected_2017, 1)) |>
dplyr::select(rowID,Names, pop_2017, observed_2017, expected_2017, SMR)
SMR_2018 <- expected_malaria_2018 |>
dplyr::mutate(SMR = round(observed_2018/expected_2018, 1)) |>
dplyr::select(rowID, Names, pop_2018, observed_2018, expected_2018, SMR)
SMR_2019 <- expected_malaria_2019 |>
dplyr::mutate(SMR = round(observed_2019/expected_2019, 1)) |>
dplyr::select(rowID, Names, pop_2019, observed_2019, expected_2019, SMR)
SMR_2020 <- expected_malaria_2020 |>
dplyr::mutate(SMR = round(observed_2020/expected_2020, 1)) |>
dplyr::select(rowID, Names, pop_2020, observed_2020, expected_2020, SMR)
# Create SMR tables ------------------------------------------------------------
SMR_table_2017 <- SMR_2017 |>
dplyr::as_tibble() |>
dplyr::rename(Health_facility = Names) |>
dplyr::select(-rowID, -geometry) |>
kable() |>
kableExtra::kable_styling(full_width = FALSE)
SMR_table_2018 <- SMR_2018 |>
dplyr::as_tibble() |>
dplyr::rename(Health_facility = Names) |>
dplyr::select(-rowID, -geometry) |>
kable() |>
kableExtra::kable_styling(full_width = FALSE)
SMR_table_2019 <- SMR_2019 |>
dplyr::as_tibble() |>
dplyr::rename(Health_facility = Names) |>
dplyr::select(-rowID, -geometry) |>
kable () |>
kableExtra::kable_styling(full_width = FALSE)
SMR_table_2020 <- SMR_2020 |>
dplyr::as_tibble() |>
dplyr::rename(Health_facility = Names) |>
dplyr::select(-rowID, -geometry) |>
kable () |>
kableExtra::kable_styling(full_width = FALSE)
SMR_table_2017| Health_facility | pop_2017 | observed_2017 | expected_2017 | SMR |
|---|---|---|---|---|
| Lodjwa Health Centre | 9923 | 564 | 826 | 0.7 |
| Nkhamenya Rural Hospital | 40154 | 2720 | 3344 | 0.8 |
| Newa Mpasazi Health Centre | 13879 | 216 | 1156 | 0.2 |
| Mpepa /Chisinga Health Centre | 27459 | 1523 | 2287 | 0.7 |
| Mnyanja Health Centre | 39950 | 1480 | 3327 | 0.4 |
| Simlemba Health Centre | 26999 | 1159 | 2249 | 0.5 |
| Ofesi Health Centre | 28098 | 1930 | 2340 | 0.8 |
| Chulu Health Centre | 27906 | 3482 | 2324 | 1.5 |
| Kapelula Health Centre | 35727 | 2970 | 2976 | 1.0 |
| Livwezi Health Centre | 22009 | 594 | 1833 | 0.3 |
| Gogode Dispensary | 13061 | 1553 | 1088 | 1.4 |
| Dwangwa Dispensary | 32704 | 1153 | 2724 | 0.4 |
| Chamama Health Facility | 20026 | 1005 | 1668 | 0.6 |
| Wimbe Health Centre | 11864 | 2558 | 988 | 2.6 |
| Chinyama | 12768 | 1140 | 1063 | 1.1 |
| Mdunga Health Centre | 18177 | 1382 | 1514 | 0.9 |
| Mtunthama Health Centre | 18744 | 1982 | 1561 | 1.3 |
| Kasungu District Hospital | 143490 | 14663 | 11951 | 1.2 |
| Chamwabvi Health Centre | 35353 | 2031 | 2945 | 0.7 |
| Linyangwa Health Centre | 17772 | 1987 | 1480 | 1.3 |
| Mziza Health Centre | 44295 | 4098 | 3689 | 1.1 |
| Kawamba Health Centre | 26385 | 3845 | 2198 | 1.7 |
| Kamboni Health Centre | 21226 | 2588 | 1768 | 1.5 |
| Khola Health Centre | 22664 | 1012 | 1888 | 0.5 |
| Santhe Health Centre | 43948 | 5668 | 3660 | 1.5 |
| Mkhota Health Centre | 23295 | 1487 | 1940 | 0.8 |
SMR_table_2018| Health_facility | pop_2018 | observed_2018 | expected_2018 | SMR |
|---|---|---|---|---|
| Lodjwa Health Centre | 10281 | 1151 | 934 | 1.2 |
| Nkhamenya Rural Hospital | 41642 | 3343 | 3785 | 0.9 |
| Newa Mpasazi Health Centre | 14248 | 434 | 1295 | 0.3 |
| Mpepa /Chisinga Health Centre | 28488 | 2616 | 2589 | 1.0 |
| Mnyanja Health Centre | 41856 | 1715 | 3804 | 0.5 |
| Simlemba Health Centre | 27455 | 1506 | 2496 | 0.6 |
| Ofesi Health Centre | 29002 | 1773 | 2636 | 0.7 |
| Chulu Health Centre | 28832 | 3330 | 2621 | 1.3 |
| Kapelula Health Centre | 37630 | 3480 | 3420 | 1.0 |
| Livwezi Health Centre | 22544 | 1128 | 2049 | 0.6 |
| Gogode Dispensary | 13368 | 2550 | 1215 | 2.1 |
| Dwangwa Dispensary | 33534 | 1216 | 3048 | 0.4 |
| Chamama Health Facility | 20372 | 1226 | 1852 | 0.7 |
| Wimbe Health Centre | 11814 | 3167 | 1074 | 2.9 |
| Chinyama | 13138 | 1673 | 1194 | 1.4 |
| Mdunga Health Centre | 18928 | 1894 | 1720 | 1.1 |
| Mtunthama Health Centre | 19074 | 3358 | 1734 | 1.9 |
| Kasungu District Hospital | 147175 | 12019 | 13377 | 0.9 |
| Chamwabvi Health Centre | 36167 | 2079 | 3287 | 0.6 |
| Linyangwa Health Centre | 18032 | 1500 | 1639 | 0.9 |
| Mziza Health Centre | 46313 | 2291 | 4210 | 0.5 |
| Kawamba Health Centre | 26253 | 3881 | 2386 | 1.6 |
| Kamboni Health Centre | 21430 | 3250 | 1948 | 1.7 |
| Khola Health Centre | 23195 | 1697 | 2108 | 0.8 |
| Santhe Health Centre | 45063 | 6195 | 4096 | 1.5 |
| Mkhota Health Centre | 23884 | 4218 | 2171 | 1.9 |
SMR_table_2019| Health_facility | pop_2019 | observed_2019 | expected_2019 | SMR |
|---|---|---|---|---|
| Lodjwa Health Centre | 10608 | 1168 | 909 | 1.3 |
| Nkhamenya Rural Hospital | 43293 | 3932 | 3709 | 1.1 |
| Newa Mpasazi Health Centre | 14780 | 626 | 1266 | 0.5 |
| Mpepa /Chisinga Health Centre | 29456 | 4169 | 2523 | 1.7 |
| Mnyanja Health Centre | 43783 | 2504 | 3751 | 0.7 |
| Simlemba Health Centre | 28076 | 1788 | 2405 | 0.7 |
| Ofesi Health Centre | 30065 | 2124 | 2576 | 0.8 |
| Chulu Health Centre | 29731 | 3537 | 2547 | 1.4 |
| Kapelula Health Centre | 39747 | 3357 | 3405 | 1.0 |
| Livwezi Health Centre | 22945 | 435 | 1966 | 0.2 |
| Gogode Dispensary | 13641 | 1469 | 1169 | 1.3 |
| Dwangwa Dispensary | 34415 | 1370 | 2948 | 0.5 |
| Chamama Health Facility | 20701 | 1127 | 1773 | 0.6 |
| Wimbe Health Centre | 11855 | 2162 | 1016 | 2.1 |
| Chinyama | 13475 | 1260 | 1154 | 1.1 |
| Mdunga Health Centre | 19960 | 1485 | 1710 | 0.9 |
| Mtunthama Health Centre | 19385 | 1718 | 1661 | 1.0 |
| Kasungu District Hospital | 151079 | 13052 | 12942 | 1.0 |
| Chamwabvi Health Centre | 36899 | 1180 | 3161 | 0.4 |
| Linyangwa Health Centre | 18279 | 2692 | 1566 | 1.7 |
| Mziza Health Centre | 48452 | 3135 | 4151 | 0.8 |
| Kawamba Health Centre | 26356 | 3469 | 2258 | 1.5 |
| Kamboni Health Centre | 21509 | 2537 | 1843 | 1.4 |
| Khola Health Centre | 23816 | 2139 | 2040 | 1.0 |
| Santhe Health Centre | 46196 | 5793 | 3957 | 1.5 |
| Mkhota Health Centre | 24429 | 2268 | 2093 | 1.1 |
SMR_table_2020| Health_facility | pop_2020 | observed_2020 | expected_2020 | SMR |
|---|---|---|---|---|
| Lodjwa Health Centre | 13081 | 1788 | 1538 | 1.2 |
| Nkhamenya Rural Hospital | 53692 | 8539 | 6313 | 1.4 |
| Newa Mpasazi Health Centre | 18311 | 2182 | 2153 | 1.0 |
| Mpepa /Chisinga Health Centre | 36317 | 5186 | 4270 | 1.2 |
| Mnyanja Health Centre | 54649 | 6117 | 6426 | 1.0 |
| Simlemba Health Centre | 34240 | 5310 | 4026 | 1.3 |
| Ofesi Health Centre | 37240 | 2323 | 4379 | 0.5 |
| Chulu Health Centre | 36638 | 7160 | 4308 | 1.7 |
| Kapelula Health Centre | 50214 | 7297 | 5904 | 1.2 |
| Livwezi Health Centre | 27786 | 1028 | 3267 | 0.3 |
| Gogode Dispensary | 16681 | 2767 | 1961 | 1.4 |
| Dwangwa Dispensary | 42282 | 2869 | 4971 | 0.6 |
| Chamama Health Facility | 25248 | 635 | 2969 | 0.2 |
| Wimbe Health Centre | 14367 | 2233 | 1689 | 1.3 |
| Chinyama | 16463 | 1605 | 1936 | 0.8 |
| Mdunga Health Centre | 25108 | 3169 | 2952 | 1.1 |
| Mtunthama Health Centre | 23501 | 1882 | 2763 | 0.7 |
| Kasungu District Hospital | 185282 | 19393 | 21785 | 0.9 |
| Chamwabvi Health Centre | 45106 | 1128 | 5304 | 0.2 |
| Linyangwa Health Centre | 22144 | 4380 | 2604 | 1.7 |
| Mziza Health Centre | 60648 | 5791 | 7131 | 0.8 |
| Kawamba Health Centre | 32076 | 7073 | 3771 | 1.9 |
| Kamboni Health Centre | 25750 | 4665 | 3028 | 1.5 |
| Khola Health Centre | 29367 | 3426 | 3453 | 1.0 |
| Santhe Health Centre | 56704 | 6556 | 6667 | 1.0 |
| Mkhota Health Centre | 29986 | 4592 | 3526 | 1.3 |
7.1 View observed and expected dry season malaria cases
# Helper function to create maps of observed and expected dry season malaria cases
create.malaria.map <- function(malaria.data,
variable = NA,
title = NA,
legend.title = NA){
# observed and expected malaria incidence map
# malaria.data: data frame containing observed and expected malaria cases
# variable: variable name (as character, in quotes e.g. "observed")
# title: map title in quotes
# legend.title: legend title in quotes
# returns:
# a tmap-element (plots a map)
tm_shape(malaria.data)+
tm_fill(col = variable,
breaks = c(0, 500, 1000, 2500, 5000, 10000, 15000, 20000, 25000),
palette = "YlOrRd",
title = legend.title)+
tm_borders(lw = 0.3)+
tm_layout(legend.position = c(0.75,"bottom"),
legend.text.size = 0.5,
legend.title.size = 0.7,
frame = FALSE)+
tm_credits(title,
position = c(0.2, 0.8),
size = 1)
}
# Invoking the function
# 2017 observed and expected malaria cases -------------------------------------
observed_malaria_2017_map <- create.malaria.map(malaria_pop_by_catchment_2017,
variable = "dr_2017",
title = "2017",
legend.title = "Observed malaria")
expected_malaria_2017_map <- create.malaria.map(expected_malaria_2017,
variable = "expected_2017",
title = "2017",
legend.title = "Expected malaria")
# 2018 observed and expected malaria cases -------------------------------------
observed_malaria_2018_map <- create.malaria.map(malaria_pop_by_catchment_2018,
variable = "dr_2018",
title = "2018",
legend.title = "Observed malaria")
expected_malaria_2018_map <- create.malaria.map(expected_malaria_2018,
variable = "expected_2018",
title = "2018",
legend.title = "Expected malaria")
# 2019 observed and expected malaria cases -------------------------------------
observed_malaria_2019_map <- create.malaria.map(malaria_pop_by_catchment_2019,
variable = "dr_2019",
title = "2019",
legend.title = "Observed malaria")
expected_malaria_2019_map <- create.malaria.map(expected_malaria_2019,
variable = "expected_2019",
title = "2019",
legend.title = "Expected malaria")
# 2020 observed and expected malaria cases -------------------------------------
observed_malaria_2020_map <- create.malaria.map(malaria_pop_by_catchment_2020,
variable = "dr_2020",
title = "2020",
legend.title = "Observed malaria")
expected_malaria_2020_map <- create.malaria.map(expected_malaria_2020,
variable = "expected_2020",
title = "2020",
legend.title = "Expected malaria")
# Layout maps ------------------------------------------------------------------
tmap::tmap_arrange(observed_malaria_2017_map, expected_malaria_2017_map,
observed_malaria_2018_map, expected_malaria_2018_map,
observed_malaria_2019_map, expected_malaria_2019_map,
observed_malaria_2020_map, expected_malaria_2020_map, ncol = 2)
Fig. 7: Observed and expected malaria incidence by health facility catchment area, Kasungu
7.2 View SMR by catchment
A ratio greater than 1.0 indicates that more malaria cases have occurred than would have been expected, while a ratio less than 1.0 indicates that less cases have occurred.
# max(SMR_2017$SMR)
# [1] 2.6
# max(SMR_2018$SMR)
# [1] 2.9
# max(SMR_2019$SMR)
# [1] 2.1
# max(SMR_2020$SMR)
# [1] 1.9
# Define function to create maps of SMR by catchment ---------------------------
create.smr.map <- function(smr.data,
variable = "SMR_category",
title = NA,
legend.title = "SMR"){
# SMR by catchment map
# smr.data: sf polygon object containing SMR by catchment data
# variable: variable name (as character, in qoutes)
# title: map title in quotes
# legend.title: legend title in qoutes
# returns:
# a tmap-element (plots a map)
# create category column
smr.data$SMR_category <- NA
# assigning labels for the SMR estimate legends
smr.category.list <- c("<0.50", "0.51 to 0.75", "0.76 to 0.99", "1.00",
"1.10 to 1.24", "1.25 to 1.49", ">1.50")
# assigning categories
smr.data$SMR_category[smr.data$SMR >= 0.00 & smr.data$SMR < 0.49] = -3
smr.data$SMR_category[smr.data$SMR >= 0.50 & smr.data$SMR < 0.75] = -2
smr.data$SMR_category[smr.data$SMR >= 0.76 & smr.data$SMR < 0.99] = -1
smr.data$SMR_category[smr.data$SMR >= 1.00 & smr.data$SMR < 1.09] = 0
smr.data$SMR_category[smr.data$SMR >= 1.10 & smr.data$SMR < 1.24] = 1
smr.data$SMR_category[smr.data$SMR >= 1.25 & smr.data$SMR < 1.49] = 2
smr.data$SMR_category[smr.data$SMR >= 1.50 & smr.data$SMR < 3.00] = 3
# generating divergent colour schemes [Blues - Light Blues – White – Light Reds – Reds]
# smr.palette <- c("#33a6fe", "#cbe6fe", "#dfeffe", "#feb1b1", "#fe8e8e","#fe0000")
tm_shape(smr.data)+
tm_fill(col = variable,
style = "cat",
palette = "-RdBu",
title = legend.title,
labels = smr.category.list)+
tm_borders(lw = 0.6)+
tm_layout(legend.position = c(0.75,"bottom"),
legend.text.size = 0.5,
legend.title.size = 0.7,
frame = FALSE)+
tm_credits(title,
position = c(0.2, 0.8),
size = 1)
}
# Invoking function ------------------------------------------------------------
SMR_2017_map <- create.smr.map(SMR_2017, title = "2017")
SMR_2018_map <- create.smr.map(SMR_2018, title = "2018")
SMR_2019_map <- create.smr.map(SMR_2019, title = "2019")
SMR_2020_map <- create.smr.map(SMR_2020, title = "2020")
# Layout maps ------------------------------------------------------------------
tmap::tmap_arrange(SMR_2017_map, SMR_2018_map, SMR_2019_map, SMR_2020_map, ncol = 2)
Fig. 8: Standardised morbidity ratio of malaria by health facility catchment
8 Calculate the proportion of the catchment population living within 1km, 2km, 3km of water bodies
First, using st_buffer, we compute 1km, 2km and 3km buffers around dry season water bodies obtained from LandSat satellite imagery using TropWet tool in Google Earth Engine. Then geometry of the buffer features are then combined resulting in resolved internal boundaries to enable extracting population values from WorldPop raster. Finally, we calculate the proportion of people in each catchment area living within water bodies.
# Combine and transform TropWet derived waterbody polygons -------------------------------
surface_waterbodies_2017 <- sf::st_buffer(dryseason_waterbodies_2017, dist = 30) |>
sf::st_union() |>
sf::st_cast("POLYGON") |>
sf::st_as_sf()
surface_waterbodies_2018 <- sf::st_buffer(dryseason_waterbodies_2018, dist = 30) |>
sf::st_union() |>
sf::st_cast("POLYGON") |>
sf::st_as_sf()
surface_waterbodies_2019 <- sf::st_buffer(dryseason_waterbodies_2019, dist = 30) |>
sf::st_union() |>
sf::st_cast("POLYGON") |>
sf::st_as_sf()
surface_waterbodies_2020 <- sf::st_buffer(dryseason_waterbodies_2020, dist = 30) |>
sf::st_union() |>
sf::st_cast("POLYGON") |>
sf::st_as_sf()
# Helper function to compute 1km, 2km and 3km buffers around the water bodies ---------------------
create.waterbody.buffer <- function(waterbody, distance, catchment){
# function for creating buffers around waterbodies
# arguments:
# waterbody: waterbody shapefile
# distance: buffer distance in meters
# catchment: catchment area shapefile
# returns:
# buffered waterbodies
# Create buffers around water bodies
buffer_radius <- sf::st_buffer(waterbody, distance)
# Dissolve the buffers
# buffer_union <- sf::st_as_sf(st_cast(st_union(buffer_radius),"MULTIPOLYGON"))
buffer_union <- sf::st_union(buffer_radius) |>
sf::st_cast("MULTIPOLYGON") |>
sf::st_as_sf()
# Assign attributes of the 'catchment' to each of the water bodies.
buffer_intersect <- sf::st_intersection(buffer_union, catchment)
buffer_intersect_sf <- sf::st_as_sf(buffer_intersect)
# Convert the MULTIPOLYGON object into several POLYGON objects
# buffer_intersect_polygons <- sf::st_cast(
# sf::st_buffer(buffer_intersect_sf,0.0), "MULTIPOLYGON") %>%
# sf::st_cast("POLYGON")
buffer_intersect_polygons <- sf::st_buffer(buffer_intersect_sf, 0.0) |>
sf::st_cast("MULTIPOLYGON") |>
sf::st_cast("POLYGON")
# Polygons being seen to be in multiple catchments
sf::st_intersects(buffer_intersect_polygons, catchment)
# Make the assumption that the attribute is constant throughout the geometry
sf::st_agr(buffer_intersect_polygons) = "constant"
sf::st_agr(catchment) = "constant"
return(out = buffer_intersect_polygons)
}
# Invoking function
# For 2017 TropWet surface water polygons --------------------------------------------------------
buffer_1km_2017 <- create.waterbody.buffer(waterbody = surface_waterbodies_2017,
distance = 1000,
catchment = malire_new)
buffer_2km_2017 <- create.waterbody.buffer(waterbody = surface_waterbodies_2017,
distance = 2000,
catchment = malire_new)
buffer_3km_2017 <- create.waterbody.buffer(waterbody = surface_waterbodies_2017,
distance = 3000,
catchment = malire_new)
# For 2018 TropWet surface water polygons --------------------------------------------------------
buffer_1km_2018 <- create.waterbody.buffer(waterbody = surface_waterbodies_2018,
distance = 1000,
catchment = malire_new)
buffer_2km_2018 <- create.waterbody.buffer(waterbody = surface_waterbodies_2018,
distance = 2000,
catchment = malire_new)
buffer_3km_2018 <- create.waterbody.buffer(waterbody = surface_waterbodies_2018,
distance = 3000,
catchment = malire_new)
# For 2019 TropWet surface water polygons ------------------------------------------------------
buffer_1km_2019 <- create.waterbody.buffer(waterbody = surface_waterbodies_2019,
distance = 1000,
catchment = malire_new)
buffer_2km_2019 <- create.waterbody.buffer(waterbody = surface_waterbodies_2019,
distance = 2000,
catchment = malire_new)
buffer_3km_2019 <- create.waterbody.buffer(waterbody = surface_waterbodies_2019,
distance = 3000,
catchment = malire_new)
# For 2020 TropWet surface water polygons ------------------------------------------------------
buffer_1km_2020 <- create.waterbody.buffer(waterbody = surface_waterbodies_2020,
distance = 1000,
catchment = malire_new)
buffer_2km_2020 <- create.waterbody.buffer(waterbody = surface_waterbodies_2020,
distance = 2000,
catchment = malire_new)
buffer_3km_2020 <- create.waterbody.buffer(waterbody = surface_waterbodies_2020,
distance = 3000,
catchment = malire_new)8.1 View the created waterbody buffers
Note that blank areas in the map represent catchment areas in which no water body was detected — this may be a limitation of using moderate resolution satellite imagery (>30m spatial resolution) to identify surface water.
# Map the buffers
create.buffer.map <- function(buffers, boundary = malire_new, title = NA){
# function for creating buffer map in ggplot
# arguments:
# buffer: waterbodies buffer polygon layer
# boundary: health facility catchment polygons
# title: main title
# returns:
# a map-element (plots a map)
ggplot(data = buffers)+
geom_sf()+
geom_sf(data = boundary,
fill = NA)+
theme_void()+
labs(title = title)
}
# Invoking the function
# For 2017 -------------------------------------------------------------------------------
buffer_1km_2017_map <- create.buffer.map(buffer_1km_2017, title = "2017: 1km Buffers")
buffer_2km_2017_map <- create.buffer.map(buffer_2km_2017, title = "2017: 2km Buffers")
buffer_3km_2017_map <- create.buffer.map(buffer_3km_2017, title = "2017: 3km Buffers")
# For 2018 --------------------------------------------------------------------------------
buffer_1km_2018_map <- create.buffer.map(buffer_1km_2018, title = "2018: 1km Buffers")
buffer_2km_2018_map <- create.buffer.map(buffer_2km_2018, title = "2018: 2km Buffers")
buffer_3km_2018_map <- create.buffer.map(buffer_3km_2018, title = "2018: 3km Buffers")
# For 2019 ---------------------------------------------------------------------------------
buffer_1km_2019_map <- create.buffer.map(buffer_1km_2019, title = "2019: 1km Buffers")
buffer_2km_2019_map <- create.buffer.map(buffer_2km_2019, title = "2019: 2km Buffers")
buffer_3km_2019_map <- create.buffer.map(buffer_3km_2019, title = "2019: 3km Buffers")
# For 2020 --------------------------------------------------------------------------------
buffer_1km_2020_map <- create.buffer.map(buffer_1km_2020, title = "2020: 1km Buffers")
buffer_2km_2020_map <- create.buffer.map(buffer_2km_2020, title = "2020: 2km Buffers")
buffer_3km_2020_map <- create.buffer.map(buffer_3km_2020, title = "2020: 3km Buffers")
grid.arrange(buffer_1km_2017_map, buffer_1km_2018_map, buffer_1km_2019_map, buffer_1km_2020_map,
buffer_2km_2017_map, buffer_2km_2018_map, buffer_2km_2019_map, buffer_2km_2020_map,
buffer_3km_2017_map, buffer_3km_2018_map, buffer_3km_2019_map, buffer_3km_2020_map, ncol = 4)
Fig 9. Buffers around dry season waterbodies in Kasungu
9 Extract the population living within waterbody buffers by catchment area
# Helper function to calculate estimated number of people living within waterbody buffers
# in each catchment area
estimate.buffer.pop <- function(catchment.population, buffers, catchment.area){
# Extract population estimates from WorldPop raster
buffers$buffer_pop <- raster::extract(catchment.population,
buffers,
fun = sum,
na.rm = TRUE)
# Find which catchment each polygon belongs to using its centroid - a point dataset
# representing the geographic center-points of the polygons
# buffer_by_catchment <- st_intersection(st_centroid(buffers), catchment.area)
buffer_by_catchment <- sf::st_centroid(buffers) |>
sf::st_intersection(catchment.area)
# Notice that the buffer_catchment is comprised of separate POLYGONS (buffer_by_catchment$x).
# The first step is to “dissolve” away these POLYGONS into one MULTIPOLYGON.
# There is no sf equivalent to the QGIS or ArcMap “dissolve” operation.
# Instead we use a combination of group_by and summarize from the dplyr package.
# Stats::aggregate from sf package, and dplyr::summarize both do essentially the same.
buffer_pop_aggregated <- buffer_by_catchment |>
dplyr::group_by(DN) |>
dplyr::summarize(buffer_pop_aggregated = round(sum(buffer_pop, na.rm = TRUE)))
buffer_pop <- merge(catchment.area, st_drop_geometry(buffer_pop_aggregated),
by = 'DN', all.x = TRUE)
return(out = buffer_pop)
}
# Invoking the function and calculating proportion of
# catchment population living within buffers
# 2017 buffer population -------------------------------------------------------
buffer_pop_1km_2017 <- estimate.buffer.pop(
kasungu_population_2017,
buffer_1km_2017,
malaria_pop_by_catchment_2017) |>
dplyr::rename(catchment_pop = pop,
buffer_pop = buffer_pop_aggregated) |>
dplyr::mutate(
prop_buffer_catchment_pop = round((buffer_pop/catchment_pop)*100))|>
dplyr::mutate(across(everything(), .fns = ~replace_na(.,0)))
buffer_pop_2km_2017 <- estimate.buffer.pop(
kasungu_population_2017,
buffer_2km_2017,
malaria_pop_by_catchment_2017) |>
dplyr::rename(catchment_pop = pop,
buffer_pop = buffer_pop_aggregated) |>
dplyr::mutate(
prop_buffer_catchment_pop = round((buffer_pop/catchment_pop)*100)) |>
dplyr::mutate(across(everything(), .fns = ~replace_na(.,0)))
buffer_pop_3km_2017 <- estimate.buffer.pop(
kasungu_population_2017,
buffer_3km_2017,
malaria_pop_by_catchment_2017) |>
dplyr::rename(catchment_pop = pop,
buffer_pop = buffer_pop_aggregated) |>
dplyr::mutate(
prop_buffer_catchment_pop = round((buffer_pop/catchment_pop)*100))|>
dplyr::mutate(across(everything(), .fns = ~replace_na(.,0)))
# 2018 buffer population -------------------------------------------------------
buffer_pop_1km_2018 <- estimate.buffer.pop(
kasungu_population_2018,
buffer_1km_2018,
malaria_pop_by_catchment_2018) |>
dplyr::rename(catchment_pop = pop,
buffer_pop = buffer_pop_aggregated) |>
dplyr::mutate(
prop_buffer_catchment_pop = round((buffer_pop/catchment_pop)*100))|>
dplyr::mutate(across(everything(), .fns = ~replace_na(.,0)))
buffer_pop_2km_2018 <- estimate.buffer.pop(
kasungu_population_2018,
buffer_2km_2018,
malaria_pop_by_catchment_2018) |>
dplyr::rename(catchment_pop = pop,
buffer_pop = buffer_pop_aggregated) |>
dplyr::mutate(
prop_buffer_catchment_pop = round((buffer_pop/catchment_pop)*100))|>
dplyr::mutate(across(everything(), .fns = ~replace_na(.,0)))
buffer_pop_3km_2018 <- estimate.buffer.pop(
kasungu_population_2018,
buffer_3km_2018,
malaria_pop_by_catchment_2018) |>
dplyr::rename(catchment_pop = pop,
buffer_pop = buffer_pop_aggregated) |>
dplyr::mutate(
prop_buffer_catchment_pop = round((buffer_pop/catchment_pop)*100))|>
dplyr::mutate(across(everything(), .fns = ~replace_na(.,0)))
# 2019 buffer population -------------------------------------------------------
buffer_pop_1km_2019 <- estimate.buffer.pop(
kasungu_population_2019,
buffer_1km_2019,
malaria_pop_by_catchment_2019) |>
dplyr::rename(catchment_pop = pop,
buffer_pop = buffer_pop_aggregated) |>
dplyr::mutate(
prop_buffer_catchment_pop = round((buffer_pop/catchment_pop)*100))|>
dplyr::mutate(across(everything(), .fns = ~replace_na(.,0)))
buffer_pop_2km_2019 <- estimate.buffer.pop(
kasungu_population_2019,
buffer_2km_2019,
malaria_pop_by_catchment_2019) |>
dplyr::rename(catchment_pop = pop,
buffer_pop = buffer_pop_aggregated) |>
dplyr::mutate(
prop_buffer_catchment_pop = round((buffer_pop/catchment_pop)*100))|>
dplyr::mutate(across(everything(), .fns = ~replace_na(.,0)))
buffer_pop_3km_2019 <- estimate.buffer.pop(
kasungu_population_2019,
buffer_3km_2019,
malaria_pop_by_catchment_2019) |>
dplyr::rename(catchment_pop = pop,
buffer_pop = buffer_pop_aggregated) |>
dplyr::mutate(
prop_buffer_catchment_pop = round((buffer_pop/catchment_pop)*100))|>
dplyr::mutate(across(everything(), .fns = ~replace_na(.,0)))
# 2020 buffer population -------------------------------------------------------
buffer_pop_1km_2020 <- estimate.buffer.pop(
kasungu_population_2020,
buffer_1km_2020,
malaria_pop_by_catchment_2020) |>
dplyr::rename(catchment_pop = pop,
buffer_pop = buffer_pop_aggregated) |>
dplyr::mutate(
prop_buffer_catchment_pop = round((buffer_pop/catchment_pop)*100))|>
dplyr::mutate(across(everything(), .fns = ~replace_na(.,0)))
buffer_pop_2km_2020 <- estimate.buffer.pop(
kasungu_population_2020,
buffer_2km_2020,
malaria_pop_by_catchment_2020) |>
dplyr::rename(catchment_pop = pop,
buffer_pop = buffer_pop_aggregated) |>
dplyr::mutate(
prop_buffer_catchment_pop = round((buffer_pop/catchment_pop)*100))|>
dplyr::mutate(across(everything(), .fns = ~replace_na(.,0)))
buffer_pop_3km_2020 <- estimate.buffer.pop(
kasungu_population_2020,
buffer_3km_2020,
malaria_pop_by_catchment_2020) |>
dplyr::rename(catchment_pop = pop,
buffer_pop = buffer_pop_aggregated) |>
dplyr::mutate(
prop_buffer_catchment_pop = round((buffer_pop/catchment_pop)*100)) |>
dplyr::mutate(across(everything(), .fns = ~replace_na(.,0)))9.1 Mapping proportion of catchment population living within waterbodies
# Helper function to create maps of proportion of people living in proximity ----------
# to water bodies in each catchment area
create.pop.proportion.map <- function(pop.data,
variable = "prop_buffer_catchment_pop",
title = NA,
legend.title = NA){
# pop.data: sf polygon object containing proportion of catchment population
# living within water bodies
# variable: variable name (as character, in qoutes)
# title: map title in quotes
# legend.title: legend title in qoutes
# returns:
# a tmap-element (plots a map)
tm_shape(pop.data)+
tm_fill(col = variable,
breaks = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100),
palette = "YlOrBr",
title = legend.title)+
tm_borders(lw = 0.3)+
tm_layout(legend.position = c(0.8,"bottom"),
legend.text.size = 0.5,
legend.title.size = 0.7,
frame = FALSE)+
tm_credits(title,
position = c(0.25, 0.75),
size = 1)
}
# Invoking function
# 2017 population proportion ---------------------------------------------------
pop_proportion_1km_2017_map <- create.pop.proportion.map(
buffer_pop_1km_2017,
title = "2017",
legend.title = "Population within \n1km buffers (%)")
pop_proportion_2km_2017_map <- create.pop.proportion.map(
buffer_pop_2km_2017,
title = "2017",
legend.title = "Population within \n2km buffers (%)")
pop_proportion_3km_2017_map <- create.pop.proportion.map(
buffer_pop_3km_2017,
title = "2017",
legend.title = "Population within \n3km buffers (%)")
# 2018 population proportion ---------------------------------------------------
pop_proportion_1km_2018_map <- create.pop.proportion.map(
buffer_pop_1km_2018,
title = "2018",
legend.title = "Population within \n1km buffers (%)")
pop_proportion_2km_2018_map <- create.pop.proportion.map(
buffer_pop_2km_2018,
title = "2018",
legend.title = "Population within \n2km buffers (%)")
pop_proportion_3km_2018_map <- create.pop.proportion.map(
buffer_pop_3km_2018,
title = "2018",
legend.title = "Population within \n3km buffers (%)")
# 2019 population proportion ---------------------------------------------------
pop_proportion_1km_2019_map <- create.pop.proportion.map(
buffer_pop_1km_2019,
title = "2019",
legend.title = "Population within \n1km buffers (%)")
pop_proportion_2km_2019_map <- create.pop.proportion.map(
buffer_pop_2km_2019,
title = "2019",
legend.title = "Population within \n2km buffers (%)")
pop_proportion_3km_2019_map <- create.pop.proportion.map(
buffer_pop_3km_2019,
title = "2019",
legend.title = "Population within \n3km buffers (%)")
# 2020 population proportion ---------------------------------------------------
pop_proportion_1km_2020_map <- create.pop.proportion.map(
buffer_pop_1km_2020,
title = "2020",
legend.title = "Population within \n1km buffers (%)")
pop_proportion_2km_2020_map <- create.pop.proportion.map(
buffer_pop_2km_2020,
title = "2020",
legend.title = "Population within \n2km buffers (%)")
pop_proportion_3km_2020_map <- create.pop.proportion.map(
buffer_pop_3km_2020,
title = "2020",
legend.title = "Population within \n3km buffers (%)")
# Layout maps ------------------------------------------------------------------
tmap::tmap_arrange(pop_proportion_1km_2017_map, pop_proportion_2km_2017_map,
pop_proportion_3km_2017_map, pop_proportion_1km_2018_map,
pop_proportion_2km_2018_map, pop_proportion_3km_2018_map,
pop_proportion_1km_2019_map, pop_proportion_2km_2019_map,
pop_proportion_3km_2019_map, pop_proportion_1km_2020_map,
pop_proportion_2km_2020_map, pop_proportion_3km_2020_map, ncol = 3)
Fig. 10. Proportion of catchment population living around water bodies
10 Scatter plots of SMR against the proportion of the catchment population living waterbody buffers
A correlation coeeficient of more than zero (cor.coeff r > 0.1) indicates some positive association between the SMR and the buffer population variables. That is, SMR of dry season malaria increases with increase in number of people surrounding water bodies.
# Helper function to tidy and bind the SMR and proportion of -------------------
# buffer-catchment population data frames
tidy.data <- function(smr.df,
proportion.pop.1km,
proprotion.pop.2km,
proportion.pop.3km){
# Convert the sf objects to data frames-------------------------------------------
smr_df <- as.data.frame(smr.df) |>
dplyr::select(rowID, Names, SMR)
proportion_pop_1km_df <- as.data.frame(proportion.pop.1km) |>
dplyr::select(rowID, prop_pop_1km = `prop_buffer_catchment_pop`)
proportion_pop_2km_df <- as.data.frame(proprotion.pop.2km) |>
dplyr::select(rowID, prop_pop_2km = `prop_buffer_catchment_pop`)
proportion_pop_3km_df <- as.data.frame(proportion.pop.3km) |>
dplyr::select(rowID, prop_pop_3km = `prop_buffer_catchment_pop`)
# Merge SMR and population data frames -----------------------------------------
combined_1 <- merge(smr_df, proportion_pop_1km_df, by = "rowID", all = TRUE)
combined_2 <- merge(proportion_pop_2km_df, proportion_pop_3km_df)
combined_fully <- merge(combined_1, combined_2, by = "rowID", all = TRUE)
}
# Invoking the function --------------------------------------------------------
smr_pop_2017 <- tidy.data(SMR_2017, buffer_pop_1km_2017, buffer_pop_2km_2017, buffer_pop_3km_2017)
smr_pop_2018 <- tidy.data(SMR_2018, buffer_pop_1km_2018, buffer_pop_2km_2018, buffer_pop_3km_2018)
smr_pop_2019 <- tidy.data(SMR_2019, buffer_pop_1km_2019, buffer_pop_2km_2019, buffer_pop_3km_2019)
smr_pop_2020 <- tidy.data(SMR_2020, buffer_pop_1km_2020, buffer_pop_2km_2020, buffer_pop_3km_2020)
# Helper function to create scatter plots --------------------------------------
create.scatter.plot <- function(smr.pop.df,
independent.var = NA,
dependent.var = "SMR",
x.label = NA,
plot.title = NA){
scatter.plot <- ggpubr::ggscatter(smr.pop.df, # data frame
x = independent.var, # x-axis variable
y = dependent.var, # y-axis variable
add = "reg.line", # Add regression line
conf.int = TRUE, # Add confidence interval
add.params = list(color = "red",
fill = "lightgray"),
palette = "jco", # journal color palette. see ?ggpar
xlab = x.label, # x-axis label
ylab = "SMR", # y-axis label
title = plot.title)+
ggpubr::stat_cor(label.y = 3)+ # Add correlation coefficient
ggpubr::font("title", size = 10, face = "bold")+
ggpubr::font("xlab", size = 10)+
ggpubr::font("ylab", size = 10)
return(scatter.plot)
}
# Invoking function
# 2017 scatter plots ------------------------------------------------------------
scatter_1km_2017 <- create.scatter.plot(
smr_pop_2017, independent.var = "prop_pop_1km",
x.label = "Percentage of catchment population \nliving in 1km buffer",
plot.title = "2017")
scatter_2km_2017 <- create.scatter.plot(
smr_pop_2017, independent.var = "prop_pop_2km",
x.label = "Percentage of catchment population \nliving in 2km buffer",
plot.title = "2017")
scatter_3km_2017 <- create.scatter.plot(
smr_pop_2017, independent.var = "prop_pop_3km",
x.label = "Percentage of catchment population \nliving in 3km buffer",
plot.title = "2017")
# 2018 scatter plots -----------------------------------------------------------
scatter_1km_2018 <- create.scatter.plot(
smr_pop_2018, independent.var = "prop_pop_1km",
x.label = "Percentage of catchment population \nliving in 1km buffer",
plot.title = "2018")
scatter_2km_2018 <- create.scatter.plot(
smr_pop_2018, independent.var = "prop_pop_2km",
x.label = "Percentage of catchment population \nliving in 2km buffer",
plot.title = "2018")
scatter_3km_2018 <- create.scatter.plot(
smr_pop_2018, independent.var = "prop_pop_3km",
x.label = "Percentage of catchment population \nliving in 3km buffer",
plot.title = "2018")
# 2019 scatter plots -----------------------------------------------------------
scatter_1km_2019 <- create.scatter.plot(
smr_pop_2019, independent.var = "prop_pop_1km",
x.label = "Percentage of catchment population \nliving in 1km buffer",
plot.title = "2019")
scatter_2km_2019 <- create.scatter.plot(
smr_pop_2019, independent.var = "prop_pop_2km",
x.label = "Percentage of catchment population \nliving in 2km buffer",
plot.title = "2019")
scatter_3km_2019 <- create.scatter.plot(
smr_pop_2019, independent.var = "prop_pop_3km",
x.label = "Percentage of catchment population \nliving in 3km buffer",
plot.title = "2019")
# 2020 scatter plots -----------------------------------------------------------
scatter_1km_2020 <- create.scatter.plot(
smr_pop_2020, independent.var = "prop_pop_1km",
x.label = "Percentage of catchment population \nliving in 1km buffer",
plot.title = "2020")
scatter_2km_2020 <- create.scatter.plot(
smr_pop_2020, independent.var = "prop_pop_2km",
x.label = "Percentage of catchment population \nliving in 2km buffer",
plot.title = "2020")
scatter_3km_2020 <- create.scatter.plot(
smr_pop_2020, independent.var = "prop_pop_3km",
x.label = "Percentage of catchment population \nliving in 3km buffer",
plot.title = "2020")
# Arrange the plots ------------------------------------------------------------
ggpubr::ggarrange(scatter_1km_2017, scatter_2km_2017, scatter_3km_2017,
scatter_1km_2018, scatter_2km_2018, scatter_3km_2018,
scatter_1km_2019, scatter_2km_2019, scatter_3km_2019,
scatter_1km_2020, scatter_2km_2020, scatter_3km_2020,
ncol = 3, nrow = 4)
Fig 11. Relationship between standardised morbidity ratio and living near waterbodies
11 Model fitting
\[ ln (E(y)) = 𝜷_0 + 𝜷_1 x + ln(𝒆_𝒊)\] where, dependent variable, 𝑦 = observed malaria cases; 𝐸(𝑦) = expected count value; independent variable, 𝑥 = percentage of people living near dams; and offset, 𝑒 = expected malaria cases.
To cope with the malaria count data coming from populations of different sizes, we specify an offset argument. This adds a constant term for each row of the data in the model. The log of the population is used in the offset term.
Summary outputs:
Estimate : the intercept (\(𝜷_0\)) and the beta coefficient estimates associated to each predictor variable.
Std.Error : the standard error of the coefficient estimates. This represents the accuracy of the coefficients. The larger the standard error, the less confident we are about the estimate.
t value : the t-statistic, which is the coefficient estimate (column 2) divided by the standard error of the estimate (column 3). For a given the predictor, the t-statistic evaluates whether or not there is significant association between the predictor and the outcome variable, i.e., whether the beta coefficient of the predictor is significantly different from zero.
Pr(>|t|) : The p-value corresponding to the t-statistic. The smaller the p-value, the more significant the estimate is.
Residuals : Provide a quick view of the distribution of the residuals, which by definition have a mean zero. Therefore, the median should not be far from zero, and the minimum (min)and maximum (max) should be roughly equal in absolute value.
Coefficients: Shows the regression beta coefficients and their statistical significance. Predictor variables, that are significantly associated to the outcome variable, are marked by stars.
Residual standard error (RSE), and R-squared (\(R^2\)) metrics tell how well the model fits to our data. An (adjusted) \(R^2\) that is close to 1 indicates that a large proportion of the variability in the outcome has been explained by the regression model. A number near 0 indicates that the regression model did not explain much of the variability in the outcome.
# Combine data for model fitting -----------------------------------------------
model_data_2017 <- merge(expected_malaria_2017, smr_pop_2017, by = "rowID", all = TRUE) |>
dplyr::select(-Names.y) |>
dplyr::rename(Names = Names.x)
model_data_2018 <- merge(expected_malaria_2018, smr_pop_2018, by = "rowID", all = TRUE) |>
dplyr::select(-Names.y) |>
dplyr::rename(Names = Names.x)
model_data_2019 <- merge(expected_malaria_2019, smr_pop_2019, by = "rowID", all = TRUE) |>
dplyr::select(-Names.y) |>
dplyr::rename(Names = Names.x)
model_data_2020 <- merge(expected_malaria_2020, smr_pop_2020, by = "rowID", all = TRUE) |>
dplyr::select(-Names.y) |>
dplyr::rename(Names = Names.x)
# Normality test ---------------------------------------------------------------
# Check whether the dependent variable follows a poisson distribution
# Dry season malaria cases appear to be non normally distributed i.e highly skewed
graphics::hist(model_data_2017$observed_2017,
main = "Histogram of 2017 dry season malaria cases ",
xlab = "Malaria cases",
las = 1)
graphics::hist(model_data_2018$observed_2018,
main = "Histogram of 2018 dry season malaria cases ",
xlab = "Malaria cases",
las = 1)
graphics::hist(model_data_2019$observed_2019,
main = "Histogram of 2019 dry season malaria cases ",
xlab = "Malaria cases",
las = 1)
graphics::hist(model_data_2020$observed_2020,
main = "Histogram of 2020 dry season malaria cases ",
xlab = "Malaria cases",
las = 1)
# Fit generalised linear model -------------------------------------------------
# Defining model parameters:
# response variable: observed_2017, observed_2018, observed_2019, observed_2020 are
# recorded dry season malaria cases in that year
# risk factor: prop_pop_1km, prop_pop_2km, prop_pop_3km are the percentage of people living
# within 1km, 2km and 3km buffers of water bodies, respectively.
# offset: expected_* is the number of malaria cases we would expect if the malaria rate
# was equal in all the catchment areas
# 2017 -------------------------------------------------------------------------
model_1km_2017 <- glm(observed_2017~prop_pop_1km+offset(log(expected_2017)),
data = model_data_2017, family = 'poisson')
# Display the statistical summary of the model
summary(model_1km_2017)##
## Call:
## glm(formula = observed_2017 ~ prop_pop_1km + offset(log(expected_2017)),
## family = "poisson", data = model_data_2017)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -34.204 -13.024 -3.552 15.308 40.886
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.290015 0.006763 -42.88 <2e-16 ***
## prop_pop_1km 0.032594 0.000570 57.18 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 12787.5 on 25 degrees of freedom
## Residual deviance: 9587.9 on 24 degrees of freedom
## AIC: 9834.2
##
## Number of Fisher Scoring iterations: 4# The estimated regression equation can be written as follow:
# observed malaria cases = -0.290015 + 0.032594 * percentage of catchment population living near dam
# Using this formula, for each change in number of people living near dams,
# we can predict the number of dry season malaria case.
#
# For example:
# for a buffer population equal zero, we can expect -0.22 malaria cases.
# for a buffer population equal 1000, we can expect round(-0.29 + 0.03 * 1000) = 30 malaria cases.
sjPlot::tab_model(model_1km_2017, digits = 3, digits.re = 3,
show.r2 = FALSE, show.aic = TRUE)| observed_2017 | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.748 | 0.738 – 0.758 | <0.001 |
| prop_pop_1km | 1.033 | 1.032 – 1.034 | <0.001 |
| Observations | 26 | ||
| AIC | 9834.177 | ||
# From the output above, the R2 is 1, meaning that the observed and the predicted
# outcome values are highly correlated, which is very good.
model_2km_2017 <- glm(observed_2017~1+prop_pop_2km+offset(log(expected_2017)),
data = model_data_2017, family = 'poisson')
summary(model_2km_2017)##
## Call:
## glm(formula = observed_2017 ~ 1 + prop_pop_2km + offset(log(expected_2017)),
## family = "poisson", data = model_data_2017)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -37.956 -15.345 -2.827 13.861 40.520
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3464047 0.0074352 -46.59 <2e-16 ***
## prop_pop_2km 0.0148180 0.0002512 58.99 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 12787.5 on 25 degrees of freedom
## Residual deviance: 9428.4 on 24 degrees of freedom
## AIC: 9674.7
##
## Number of Fisher Scoring iterations: 4sjPlot::tab_model(model_2km_2017, digits = 3, digits.re = 3,
show.r2 = FALSE, show.aic = TRUE)| observed_2017 | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.707 | 0.697 – 0.718 | <0.001 |
| prop_pop_2km | 1.015 | 1.014 – 1.015 | <0.001 |
| Observations | 26 | ||
| AIC | 9674.729 | ||
model_3km_2017 <- glm(observed_2017~1+prop_pop_3km+offset(log(expected_2017)),
data = model_data_2017, family = 'poisson')
summary(model_3km_2017)##
## Call:
## glm(formula = observed_2017 ~ 1 + prop_pop_3km + offset(log(expected_2017)),
## family = "poisson", data = model_data_2017)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -41.647 -12.328 -0.855 12.107 42.421
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4032949 0.0086614 -46.56 <2e-16 ***
## prop_pop_3km 0.0104255 0.0001884 55.34 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 12787.5 on 25 degrees of freedom
## Residual deviance: 9695.7 on 24 degrees of freedom
## AIC: 9942
##
## Number of Fisher Scoring iterations: 4sjPlot::tab_model(model_3km_2017, digits = 3, digits.re = 3,
show.r2 = FALSE, show.aic = TRUE)| observed_2017 | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.668 | 0.657 – 0.680 | <0.001 |
| prop_pop_3km | 1.010 | 1.010 – 1.011 | <0.001 |
| Observations | 26 | ||
| AIC | 9942.005 | ||
# 2018 -------------------------------------------------------------------------
model_1km_2018 <- glm(observed_2018~1+prop_pop_1km+offset(log(expected_2018)),
data = model_data_2018, family = 'poisson')
summary(model_1km_2018)##
## Call:
## glm(formula = observed_2018 ~ 1 + prop_pop_1km + offset(log(expected_2018)),
## family = "poisson", data = model_data_2018)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -39.078 -16.578 4.206 15.381 44.099
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2872887 0.0069329 -41.44 <2e-16 ***
## prop_pop_1km 0.0233952 0.0004486 52.15 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 16341 on 25 degrees of freedom
## Residual deviance: 13701 on 24 degrees of freedom
## AIC: 13954
##
## Number of Fisher Scoring iterations: 4sjPlot::tab_model(model_1km_2018, digits = 3, digits.re = 3,
show.r2 = FALSE, show.aic = TRUE)| observed_2018 | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.750 | 0.740 – 0.761 | <0.001 |
| prop_pop_1km | 1.024 | 1.023 – 1.025 | <0.001 |
| Observations | 26 | ||
| AIC | 13953.927 | ||
model_2km_2018 <- glm(observed_2018~1+prop_pop_2km+offset(log(expected_2018)),
data = model_data_2018, family = 'poisson')
summary(model_2km_2018)##
## Call:
## glm(formula = observed_2018 ~ 1 + prop_pop_2km + offset(log(expected_2018)),
## family = "poisson", data = model_data_2018)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -39.890 -17.663 5.323 15.116 43.296
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2984143 0.0079252 -37.65 <2e-16 ***
## prop_pop_2km 0.0092187 0.0002069 44.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 16341 on 25 degrees of freedom
## Residual deviance: 14352 on 24 degrees of freedom
## AIC: 14604
##
## Number of Fisher Scoring iterations: 4sjPlot::tab_model(model_2km_2018, digits = 3, digits.re = 3,
show.r2 = FALSE, show.aic = TRUE)| observed_2018 | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.742 | 0.731 – 0.754 | <0.001 |
| prop_pop_2km | 1.009 | 1.009 – 1.010 | <0.001 |
| Observations | 26 | ||
| AIC | 14604.406 | ||
model_3km_2018 <- glm(observed_2018~1+prop_pop_3km+offset(log(expected_2018)),
data = model_data_2018, family = 'poisson')
summary(model_3km_2018)##
## Call:
## glm(formula = observed_2018 ~ 1 + prop_pop_3km + offset(log(expected_2018)),
## family = "poisson", data = model_data_2018)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -42.046 -17.708 3.291 17.576 41.607
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4035469 0.0100849 -40.02 <2e-16 ***
## prop_pop_3km 0.0080266 0.0001803 44.53 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 16341 on 25 degrees of freedom
## Residual deviance: 14302 on 24 degrees of freedom
## AIC: 14555
##
## Number of Fisher Scoring iterations: 4sjPlot::tab_model(model_3km_2018, digits = 3, digits.re = 3,
show.r2 = FALSE, show.aic = TRUE)| observed_2018 | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.668 | 0.655 – 0.681 | <0.001 |
| prop_pop_3km | 1.008 | 1.008 – 1.008 | <0.001 |
| Observations | 26 | ||
| AIC | 14554.845 | ||
# 2019 -------------------------------------------------------------------------
model_1km_2019 <- glm(observed_2019~1+prop_pop_1km+offset(log(expected_2019)),
data = model_data_2019, family = 'poisson')
summary(model_1km_2019)##
## Call:
## glm(formula = observed_2019 ~ 1 + prop_pop_1km + offset(log(expected_2019)),
## family = "poisson", data = model_data_2019)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -41.345 -10.045 2.782 10.830 38.276
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1858493 0.0082288 -22.59 <2e-16 ***
## prop_pop_1km 0.0101514 0.0003897 26.05 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 10738 on 25 degrees of freedom
## Residual deviance: 10063 on 24 degrees of freedom
## AIC: 10314
##
## Number of Fisher Scoring iterations: 4sjPlot::tab_model(model_1km_2019, digits = 3, digits.re = 3,
show.r2 = FALSE, show.aic = TRUE)| observed_2019 | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.830 | 0.817 – 0.844 | <0.001 |
| prop_pop_1km | 1.010 | 1.009 – 1.011 | <0.001 |
| Observations | 26 | ||
| AIC | 10313.731 | ||
model_2km_2019 <- glm(observed_2019~1+prop_pop_2km+offset(log(expected_2019)),
data = model_data_2019, family = 'poisson')
summary(model_2km_2019)##
## Call:
## glm(formula = observed_2019 ~ 1 + prop_pop_2km + offset(log(expected_2019)),
## family = "poisson", data = model_data_2019)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -41.706 -13.737 1.651 13.668 33.174
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0788997 0.0095633 -8.250 <2e-16 ***
## prop_pop_2km 0.0017563 0.0001943 9.037 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 10738 on 25 degrees of freedom
## Residual deviance: 10656 on 24 degrees of freedom
## AIC: 10907
##
## Number of Fisher Scoring iterations: 4sjPlot::tab_model(model_2km_2019, digits = 3, digits.re = 3,
show.r2 = FALSE, show.aic = TRUE)| observed_2019 | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.924 | 0.907 – 0.942 | <0.001 |
| prop_pop_2km | 1.002 | 1.001 – 1.002 | <0.001 |
| Observations | 26 | ||
| AIC | 10906.895 | ||
model_3km_2019 <- glm(observed_2019~1+prop_pop_3km+offset(log(expected_2019)),
data = model_data_2019, family = 'poisson')
summary(model_3km_2019)##
## Call:
## glm(formula = observed_2019 ~ 1 + prop_pop_3km + offset(log(expected_2019)),
## family = "poisson", data = model_data_2019)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -41.991 -13.901 1.614 13.634 32.809
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.0733555 0.0114347 -6.415 1.41e-10 ***
## prop_pop_3km 0.0010803 0.0001584 6.821 9.03e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 10738 on 25 degrees of freedom
## Residual deviance: 10691 on 24 degrees of freedom
## AIC: 10942
##
## Number of Fisher Scoring iterations: 4sjPlot::tab_model(model_3km_2019, digits = 3, digits.re = 3,
show.r2 = FALSE, show.aic = TRUE)| observed_2019 | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.929 | 0.909 – 0.950 | <0.001 |
| prop_pop_3km | 1.001 | 1.001 – 1.001 | <0.001 |
| Observations | 26 | ||
| AIC | 10942.072 | ||
# 2020 -------------------------------------------------------------------------
model_1km_2020 <- glm(observed_2020~1+prop_pop_1km+offset(log(expected_2020)),
data = model_data_2020, family = 'poisson')
summary(model_1km_2020)##
## Call:
## glm(formula = observed_2020 ~ 1 + prop_pop_1km + offset(log(expected_2020)),
## family = "poisson", data = model_data_2020)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -69.717 -15.903 2.037 17.150 48.288
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0054352 0.0046182 1.177 0.239
## prop_pop_1km -0.0005935 0.0003934 -1.509 0.131
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 20816 on 25 degrees of freedom
## Residual deviance: 20814 on 24 degrees of freedom
## AIC: 21078
##
## Number of Fisher Scoring iterations: 4sjPlot::tab_model(model_1km_2020, digits = 3, digits.re = 3,
show.r2 = FALSE, show.aic = TRUE)| observed_2020 | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 1.005 | 0.996 – 1.015 | 0.239 |
| prop_pop_1km | 0.999 | 0.999 – 1.000 | 0.131 |
| Observations | 26 | ||
| AIC | 21077.777 | ||
model_2km_2020 <- glm(observed_2020~1+prop_pop_2km+offset(log(expected_2020)),
data = model_data_2020, family = 'poisson')
summary(model_2km_2020)##
## Call:
## glm(formula = observed_2020 ~ 1 + prop_pop_2km + offset(log(expected_2020)),
## family = "poisson", data = model_data_2020)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -69.739 -16.679 2.382 17.531 47.759
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.272e-03 5.097e-03 -0.446 0.656
## prop_pop_2km 9.772e-05 1.802e-04 0.542 0.588
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 20816 on 25 degrees of freedom
## Residual deviance: 20816 on 24 degrees of freedom
## AIC: 21080
##
## Number of Fisher Scoring iterations: 4sjPlot::tab_model(model_2km_2020, digits = 3, digits.re = 3,
show.r2 = FALSE, show.aic = TRUE)| observed_2020 | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.998 | 0.988 – 1.008 | 0.656 |
| prop_pop_2km | 1.000 | 1.000 – 1.000 | 0.588 |
| Observations | 26 | ||
| AIC | 21079.760 | ||
model_3km_2020 <- glm(observed_2020~1+prop_pop_3km+offset(log(expected_2020)),
data = model_data_2020, family = 'poisson')
summary(model_3km_2020)##
## Call:
## glm(formula = observed_2020 ~ 1 + prop_pop_3km + offset(log(expected_2020)),
## family = "poisson", data = model_data_2020)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -69.748 -16.602 2.349 17.485 47.813
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.586e-03 5.501e-03 -0.288 0.773
## prop_pop_3km 4.136e-05 1.219e-04 0.339 0.734
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 20816 on 25 degrees of freedom
## Residual deviance: 20816 on 24 degrees of freedom
## AIC: 21080
##
## Number of Fisher Scoring iterations: 4sjPlot::tab_model(model_3km_2020, digits = 3, digits.re = 3,
show.r2 = FALSE, show.aic = TRUE)| observed_2020 | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.998 | 0.988 – 1.009 | 0.773 |
| prop_pop_3km | 1.000 | 1.000 – 1.000 | 0.734 |
| Observations | 26 | ||
| AIC | 21079.939 | ||
12 Assess the performance and accuracy of the regression model.
Here, we evaluate how well the Poisson regression model is predicting the outcome of a new test data that have not been used to build the model i.e how close the prediction is close to the real value.
Two important metrics have been used to assess the performance of the predictive regression model:
Root Mean Squared Error, which measures the model prediction error. It corresponds to the average difference between the observed known values of the outcome and the predicted value by the model.RMSE is computed as RMSE = mean((observed - predicted)^2) |> sqrt(). The lower the RMSE, the better the model.
\[RMSE = \sqrt \frac {\Sigma ( y - \hat{y} )^2}{N}\]
Pseudo R-squared goodness-of-fit measure for count data in Poisson regression which is nonlinear and computed using deviance statistics:
\[D=2∑ni=1{Yilog(Yi/μi)−(Yi−μi)}\] where \[μi=exp(β^0+β^1X1+...+β^pXp)\] denotes the predicted mean for observation i based on the estimated model parameters. AJKOER (https://stats.stackexchange.com/users/54013/ajkoer), Basic R-Squared in Poisson Regression, URL (version: 2020-06-28): https://stats.stackexchange.com/q/474500
# Tidy model assessment data ---------------------------------------------------
model_assessment_data_2017 <- model_data_2017 |>
dplyr::as_tibble() |>
dplyr::select(Names, observed_2017, expected_2017,
prop_pop_1km, prop_pop_2km, prop_pop_3km) |>
dplyr::glimpse()## Rows: 26
## Columns: 6
## $ Names <chr> "Lodjwa Health Centre", "Nkhamenya Rural Hospital", "New~
## $ observed_2017 <int> 564, 2720, 216, 1523, 1480, 1159, 1930, 3482, 2970, 594,~
## $ expected_2017 <dbl> 826, 3344, 1156, 2287, 3327, 2249, 2340, 2324, 2976, 183~
## $ prop_pop_1km <dbl> 10, 4, 1, 0, 2, 2, 0, 7, 2, 8, 6, 9, 2, 21, 11, 0, 21, 1~
## $ prop_pop_2km <dbl> 7, 16, 7, 0, 6, 8, 2, 23, 8, 20, 19, 29, 9, 53, 30, 0, 6~
## $ prop_pop_3km <dbl> 19, 30, 14, 0, 19, 17, 2, 45, 16, 40, 39, 54, 16, 80, 46~model_assessment_data_2018 <- model_data_2018 |>
dplyr::as_tibble() |>
dplyr::select(Names, observed_2018, expected_2018,
prop_pop_1km, prop_pop_2km, prop_pop_3km) |>
dplyr::glimpse()## Rows: 26
## Columns: 6
## $ Names <chr> "Lodjwa Health Centre", "Nkhamenya Rural Hospital", "New~
## $ observed_2018 <int> 1151, 3343, 434, 2616, 1715, 1506, 1773, 3330, 3480, 112~
## $ expected_2018 <dbl> 934, 3785, 1295, 2589, 3804, 2496, 2636, 2621, 3420, 204~
## $ prop_pop_1km <dbl> 0, 3, 1, 4, 3, 3, 2, 11, 9, 8, 9, 10, 3, 20, 14, 7, 25, ~
## $ prop_pop_2km <dbl> 0, 6, 7, 16, 11, 10, 5, 39, 27, 25, 22, 37, 12, 54, 33, ~
## $ prop_pop_3km <dbl> 0, 13, 16, 31, 30, 23, 10, 65, 51, 54, 44, 61, 22, 80, 4~model_assessment_data_2019 <- model_data_2019 |>
dplyr::as_tibble() |>
dplyr::select(Names, observed_2019, expected_2019,
prop_pop_1km, prop_pop_2km, prop_pop_3km) |>
dplyr::glimpse()## Rows: 26
## Columns: 6
## $ Names <chr> "Lodjwa Health Centre", "Nkhamenya Rural Hospital", "New~
## $ observed_2019 <int> 1168, 3932, 626, 4169, 2504, 1788, 2124, 3537, 3357, 435~
## $ expected_2019 <dbl> 909, 3709, 1266, 2523, 3751, 2405, 2576, 2547, 3405, 196~
## $ prop_pop_1km <dbl> 10, 10, 5, 3, 7, 11, 5, 27, 10, 17, 10, 22, 2, 21, 14, 8~
## $ prop_pop_2km <dbl> 3, 29, 18, 11, 17, 32, 20, 60, 35, 43, 22, 64, 13, 54, 3~
## $ prop_pop_3km <dbl> 10, 58, 44, 19, 36, 55, 36, 86, 54, 72, 42, 85, 18, 81, ~model_assessment_data_2020 <- model_data_2020 |>
dplyr::as_tibble() |>
dplyr::select(Names, observed_2020, expected_2020,
prop_pop_1km, prop_pop_2km, prop_pop_3km) |>
dplyr::glimpse()## Rows: 26
## Columns: 6
## $ Names <chr> "Lodjwa Health Centre", "Nkhamenya Rural Hospital", "New~
## $ observed_2020 <int> 1788, 8539, 2182, 5186, 6117, 5310, 2323, 7160, 7297, 10~
## $ expected_2020 <dbl> 1538, 6313, 2153, 4270, 6426, 4026, 4379, 4308, 5904, 32~
## $ prop_pop_1km <dbl> 1, 5, 2, 0, 1, 3, 0, 13, 0, 7, 7, 9, 2, 21, 11, 0, 20, 1~
## $ prop_pop_2km <dbl> 4, 20, 8, 0, 2, 10, 3, 36, 0, 17, 20, 34, 8, 53, 28, 0, ~
## $ prop_pop_3km <dbl> 9, 42, 17, 0, 5, 22, 2, 54, 0, 38, 40, 58, 14, 80, 45, 0~# To do: LOOCV(Leave One Out Cross-Validation) and k-fold Cross Validation
# Split the data into training and test set ------------------------------------
set.seed(2) # generate random numbers
split_2017 <- caTools::sample.split(model_assessment_data_2017,
SplitRatio = 0.8) # use 80% of the data for training
train_2017 <- subset(model_assessment_data_2017, split = "TRUE")
test_2017 <- subset(model_assessment_data_2017, split = "FALSE")
# Build model
model_2017 <- glm(observed_2017~1+prop_pop_1km+offset(log(expected_2017)),
data = train_2017, family = 'poisson')
summary(model_2017)##
## Call:
## glm(formula = observed_2017 ~ 1 + prop_pop_1km + offset(log(expected_2017)),
## family = "poisson", data = train_2017)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -34.204 -13.024 -3.552 15.308 40.886
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.290015 0.006763 -42.88 <2e-16 ***
## prop_pop_1km 0.032594 0.000570 57.18 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 12787.5 on 25 degrees of freedom
## Residual deviance: 9587.9 on 24 degrees of freedom
## AIC: 9834.2
##
## Number of Fisher Scoring iterations: 4# Prediction
predicted_2017 <- predict(model_2017, test_2017, type = "response")
# Compare predicted vs actual dry season malaria cases
par(mfrow = c(2, 1))
plot(test_2017$observed_2017, type = "l", lty = 1.8, col = "blue")
plot(predicted_2017, type = "l", lty = 1.8, col = "red", add = TRUE)
par(mfrow = c(1, 1)) # Create a 2 x 2 plotting matrix
# Split the data into training and test set
set.seed(123) # generate a sequence of random numbers
# 2017 -------------------------------------------------------------------------
training_samples_2017 <- model_assessment_data_2017$observed_2017 |>
caret::createDataPartition(p = 0.8, list = FALSE) # use 80% of the data for training
train_data_2017 <- model_assessment_data_2017[training_samples_2017, ]
test_data_2017 <- model_assessment_data_2017[-training_samples_2017, ]
# 2018
training_samples_2018 <- model_assessment_data_2018$observed_2018 |>
caret::createDataPartition(p = 0.8, list = FALSE) # see ?createDataPartition
train_data_2018 <- model_assessment_data_2018[training_samples_2018, ]
test_data_2018 <- model_assessment_data_2018[-training_samples_2018, ]
# 2019
training_samples_2019 <- model_assessment_data_2019$observed_2019 |>
caret::createDataPartition(p = 0.8, list = FALSE)
train_data_2019 <- model_assessment_data_2019[training_samples_2019, ]
test_data_2019 <- model_assessment_data_2019[-training_samples_2019, ]
# 2020
training_samples_2020 <- model_assessment_data_2020$observed_2020 |>
caret::createDataPartition(p = 0.8, list = FALSE)
train_data_2020 <- model_assessment_data_2020[training_samples_2020, ]
test_data_2020 <- model_assessment_data_2020[-training_samples_2020, ]
# Make predictions using the test data in order to evaluate the performance
# of our regression model aka goodness-of-fit. The "response" type of prediction
# is on the scale of the response variable. Thus for a default binomial model
# the default predictions are of log-odds (probabilities on logit scale) and
# type = "response" gives the predicted probabilities.
# 2017 -------------------------------------------------------------------------
predictions_1km_2017 <- model_1km_2017 |>
stats::predict.glm(test_data_2017, type = "response") # see ?stats::predict.glm
predictions_2km_2017 <- model_2km_2017 |>
stats::predict.glm(test_data_2017, type = "response")
# 2018
predictions_1km_2018 <- model_1km_2018 |>
stats::predict.glm(test_data_2018, type = "response")
predictions_2km_2018 <- model_2km_2018 |>
stats::predict.glm(test_data_2018, type = "response")
# 2019
predictions_1km_2019 <- model_1km_2019 |>
stats::predict.glm(test_data_2019, type = "response")
predictions_2km_2019 <- model_2km_2019 |>
stats::predict.glm(test_data_2019, type = "response")
# 2020
predictions_1km_2020 <- model_1km_2020 |>
stats::predict.glm(test_data_2020, type = "response")
predictions_2km_2020 <- model_2km_2020 |>
stats::predict.glm(test_data_2020, type = "response")
# Model performance ------------------------------------------------------------
# (a) Compute the prediction error, RMSE. The lower the RMSE, the better the model
# 2017
caret::RMSE(predictions_1km_2017, test_data_2017$observed_2017)## [1] 753.5512caret::RMSE(predictions_2km_2017, test_data_2017$observed_2017)## [1] 656.2454# 2018
caret::RMSE(predictions_1km_2018, test_data_2018$observed_2018)## [1] 1801.58caret::RMSE(predictions_2km_2018, test_data_2018$observed_2018)## [1] 1682.038# 2019
caret::RMSE(predictions_1km_2019, test_data_2019$observed_2019)## [1] 1376.436caret::RMSE(predictions_2km_2019, test_data_2019$observed_2019)## [1] 1348.131# 2020
caret::RMSE(predictions_1km_2020, test_data_2020$observed_2020)## [1] 2398.95caret::RMSE(predictions_2km_2020, test_data_2020$observed_2020)## [1] 2400.144# (b) Compute pseudo R-square
# We express the goodness of fit of the Poisson regression model by widely
# used variants of pseudo R squared statistics, most of which are based on
# the deviance of the model:
# - the Aldrich-Nelson pseudo-R2 with the Veall-Zimmermann correction, which
# is the best approximation of the McKelvey-Zavoina.
# Efron, Aldrich-Nelson, McFadden and Nagelkerke approaches severely underestimate
# the "true R2". -----------------------------------------------------------------
# DescTools::PseudoR2(model_1km_2017, "all")
round(DescTools::PseudoR2(model_1km_2017, c("AldrichNelson", "AIC", "LogLikNull",
"LogLik", "G2", "VeallZimmermann")), 2)## AldrichNelson AIC G2 VeallZimmermann
## 0.99 9834.18 3199.65 0.99round(DescTools::PseudoR2(model_2km_2017, c("AldrichNelson", "AIC", "LogLikNull",
"LogLik", "G2", "VeallZimmermann")), 2)## AldrichNelson AIC G2 VeallZimmermann
## 0.99 9674.73 3359.10 0.99round(DescTools::PseudoR2(model_1km_2018, c("AldrichNelson", "AIC", "LogLikNull",
"LogLik", "G2", "VeallZimmermann")), 2)## AldrichNelson AIC G2 VeallZimmermann
## 0.99 13953.93 2639.84 0.99round(DescTools::PseudoR2(model_2km_2018, c("AldrichNelson", "AIC", "LogLikNull",
"LogLik", "G2", "VeallZimmermann")), 2)## AldrichNelson AIC G2 VeallZimmermann
## 0.99 14604.41 1989.36 0.99round(DescTools::PseudoR2(model_1km_2019, c("AldrichNelson", "AIC", "LogLikNull",
"LogLik", "G2", "VeallZimmermann")), 2)## AldrichNelson AIC G2 VeallZimmermann
## 0.96 10313.73 675.17 0.97round(DescTools::PseudoR2(model_2km_2019, c("AldrichNelson", "AIC", "LogLikNull",
"LogLik", "G2", "VeallZimmermann")), 2)## AldrichNelson AIC G2 VeallZimmermann
## 0.76 10906.89 82.01 0.76round(DescTools::PseudoR2(model_1km_2020, c("AldrichNelson", "AIC", "LogLikNull",
"LogLik", "G2", "VeallZimmermann")), 2)## AldrichNelson AIC G2 VeallZimmermann
## 0.08 21077.78 2.28 0.08round(DescTools::PseudoR2(model_2km_2020, c("AldrichNelson", "AIC", "LogLikNull",
"LogLik", "G2", "VeallZimmermann")), 2)## AldrichNelson AIC G2 VeallZimmermann
## 0.01 21079.76 0.29 0.0112.0.1 Check for collinearity, normality or heteroscedasticity
# Check for collinearity, normality or heteroscedasticity --------------------
performance::check_model(model_1km_2017, theme = "see::theme_modern")
performance::check_model(model_2km_2017, theme = "see::theme_modern")
performance::check_model(model_1km_2018, theme = "see::theme_modern")
performance::check_model(model_2km_2018, theme = "see::theme_modern")
performance::check_model(model_1km_2019, theme = "see::theme_modern")
performance::check_model(model_2km_2019, theme = "see::theme_modern")
performance::check_model(model_1km_2020, theme = "see::theme_modern")
performance::check_model(model_2km_2020, theme = "see::theme_modern")
# Model performance summaries --------------------------------------------------
# Compute indices of model performance for the regression models and
# compare the quality of the models.
# Note that all score value do not necessarily sum up to 100%. See ?compare_performance
model_comparison <- performance::compare_performance(model_1km_2017, model_2km_2017,
model_1km_2018, model_2km_2018,
model_1km_2019, model_2km_2019,
model_1km_2020, model_2km_2020,
metrics = "common", rank = TRUE)
model_comparison## # Comparison of Model Performance Indices
##
## Name | Model | AIC | BIC | Nagelkerke's R2 | RMSE | Performance-Score
## -----------------------------------------------------------------------------------------------
## model_2km_2017 | glm | 9674.729 | 9677.245 | 1.000 | 906.793 | 100.00%
## model_1km_2017 | glm | 9834.177 | 9836.693 | 1.000 | 934.411 | 98.50%
## model_1km_2019 | glm | 10313.731 | 10316.247 | 1.000 | 952.120 | 95.89%
## model_2km_2019 | glm | 10906.895 | 10909.411 | 0.957 | 972.461 | 91.62%
## model_1km_2018 | glm | 13953.927 | 13956.443 | 1.000 | 1338.635 | 68.77%
## model_2km_2018 | glm | 14604.406 | 14606.922 | 1.000 | 1363.406 | 65.20%
## model_1km_2020 | glm | 21077.777 | 21080.293 | 0.084 | 1764.232 | 2.08%
## model_2km_2020 | glm | 21079.760 | 21082.277 | 0.011 | 1772.332 | 0.00%12.0.2 Visualization of indices of models’ performance
plot(performance::compare_performance(model_1km_2017, model_2km_2017,
model_1km_2018, model_2km_2018,
model_1km_2019, model_2km_2019,
model_1km_2020, model_2km_2020,
metrics = "common", rank = TRUE))
12.0.3 Check how well the fitted values line up with the observations
The fitted values appear to line up particularly well with the observed data, suggesting that prop_pop_* (i.e., proportion of catchment population living near water bodies) can help us understand malaria risk in the catchment areas.
# Helper function to create scatter plots to see how well
# fitted values line up with observed malaria cases
plot.fitted.values <- function(fitted.values.df, model.df, title){
# Remove missing values from model data since
# model fitting deletes missing observations
model.df.complete <- model.df |>
tidyr::drop_na() |>
dplyr::rename_at(vars(starts_with("observed_")), ~ str_c("observed"))
# Plot fitted versus observed values
scatter.plot <- ggplot2::ggplot()+
ggplot2::geom_point(aes(fitted.values.df$fitted.values,
model.df.complete$observed))+
ggplot2::theme_classic()+
ggplot2::labs(x = "Fitted values",
y = "Observed values",
title = title)
return(scatter.plot)
}
# Invoking function
# 2017 -------------------------------------------------------------------------
fitted_1km_2017 <- plot.fitted.values(model_1km_2017, model_data_2017, "2017: 1km model")
fitted_2km_2017 <- plot.fitted.values(model_2km_2017, model_data_2017, "2017: 2km model")
fitted_3km_2017 <- plot.fitted.values(model_3km_2017, model_data_2017, "2017: 3km model")
# 2018 -------------------------------------------------------------------------
fitted_1km_2018 <- plot.fitted.values(model_1km_2018, model_data_2018, "2018: 1km model")
fitted_2km_2018 <- plot.fitted.values(model_2km_2018, model_data_2018, "2018: 2km model")
fitted_3km_2018 <- plot.fitted.values(model_3km_2018, model_data_2018, "2018: 3km model")
# 2019 -------------------------------------------------------------------------
fitted_1km_2019 <- plot.fitted.values(model_1km_2019, model_data_2019, "2019: 1km model")
fitted_2km_2019 <- plot.fitted.values(model_2km_2019, model_data_2019, "2019: 2km model")
fitted_3km_2019 <- plot.fitted.values(model_3km_2019, model_data_2020, "2019: 3km model")
# 2020 -------------------------------------------------------------------------
fitted_1km_2020 <- plot.fitted.values(model_1km_2020, model_data_2020, "2020: 1km model")
fitted_2km_2020 <- plot.fitted.values(model_2km_2020, model_data_2020, "2020: 2km model")
fitted_3km_2020 <- plot.fitted.values(model_3km_2020, model_data_2020, "2020: 3km model")
# Layout scatter plots ---------------------------------------------------------
cowplot::plot_grid(fitted_1km_2017, fitted_2km_2017,
fitted_1km_2018, fitted_2km_2018,
fitted_1km_2019, fitted_2km_2019,
fitted_1km_2020, fitted_2km_2020,
ncol = 2, nrow = 4)
Fig. 12. How well the percentage of catchment population living around water bodies explain observed malaria incidence
12.1 Test for residual spatial autocorrelation using adjacency as criterion
# Prep data
prep.spatial.dependency.data <- function(sf_2017, sf_2018, sf_2019, sf_2020){
df_2017 <- sf_2017 |>
dplyr::as_tibble() |>
dplyr::rename(observed_2018 = dr_2018,
observed_2019 = dr_2019,
observed_2020 = dr_2020,
SMR_2017 = SMR) |>
dplyr::select(rowID, Names, SMR_2017, geometry)
df_2018 <- sf_2018 |>
dplyr::as_tibble() |>
dplyr::rename(SMR_2018 = SMR) |>
dplyr::select(rowID, SMR_2018)
df_2019 <- sf_2019 |>
dplyr::as_tibble() |>
dplyr::rename(SMR_2019 = SMR) |>
dplyr::select(rowID, SMR_2019)
df_2020 <- sf_2020 |>
dplyr::as_tibble() |>
dplyr::rename(SMR_2020 = SMR) |>
dplyr::select(rowID, SMR_2020)
spatial_dependency_data <- merge(
merge(
merge(
df_2017, df_2018, by = "rowID", all = TRUE),
df_2019, by = "rowID", all = TRUE),
df_2020, by = "rowID", all = TRUE)
return(spatial_dependency_data)
}
# Invoking function ------------------------------------------------------------
spatial_dependency_data <- prep.spatial.dependency.data(model_data_2017,
model_data_2018,
model_data_2019,
model_data_2020)
# Find adjacent polygons i.e., make neigbhour list,
# Contiguity neighbors - all that share a boundary point
spatial_dependency_shp <- sf::st_as_sf(spatial_dependency_data) |>
as("Spatial")
catchment_neighbours <- spdep::poly2nb(spatial_dependency_shp) # Queen contiguity
summary(catchment_neighbours)## Neighbour list object:
## Number of regions: 26
## Number of nonzero links: 98
## Percentage nonzero weights: 14.49704
## Average number of links: 3.769231
## Link number distribution:
##
## 1 2 3 4 5 6 7 8
## 2 3 8 5 5 1 1 1
## 2 least connected regions:
## 1 26 with 1 link
## 1 most connected region:
## 18 with 8 links# Get coordinates from catchment polygons
# Get center points of each catchment area
coords <- coordinates(spatial_dependency_shp)
# View the connections
{plot(spatial_dependency_shp, asp = 1)+
plot(catchment_neighbours, coords, col = "blue", add = TRUE)}
Fig. 13. Neighbourhood matrix
## integer(0)# Run a Moran I test on SMR
moran.test(spatial_dependency_data$SMR_2017,
nb2listw(catchment_neighbours))##
## Moran I test under randomisation
##
## data: spatial_dependency_data$SMR_2017
## weights: nb2listw(catchment_neighbours)
##
## Moran I statistic standard deviate = 1.0451, p-value = 0.148
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.10287363 -0.04000000 0.01868994moran.test(spatial_dependency_data$SMR_2018,
nb2listw(catchment_neighbours))##
## Moran I test under randomisation
##
## data: spatial_dependency_data$SMR_2018
## weights: nb2listw(catchment_neighbours)
##
## Moran I statistic standard deviate = 1.4625, p-value = 0.0718
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.16260895 -0.04000000 0.01919258moran.test(spatial_dependency_data$SMR_2019,
nb2listw(catchment_neighbours))##
## Moran I test under randomisation
##
## data: spatial_dependency_data$SMR_2019
## weights: nb2listw(catchment_neighbours)
##
## Moran I statistic standard deviate = 0.64086, p-value = 0.2608
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.05113078 -0.04000000 0.02022102moran.test(spatial_dependency_data$SMR_2020,
nb2listw(catchment_neighbours))##
## Moran I test under randomisation
##
## data: spatial_dependency_data$SMR_2020
## weights: nb2listw(catchment_neighbours)
##
## Moran I statistic standard deviate = 1.0093, p-value = 0.1564
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.10407174 -0.04000000 0.02037431# Run a Moran I MC test on SMR
moran.mc(spatial_dependency_data$SMR_2017,
nb2listw(catchment_neighbours),
nsim = 999)##
## Monte-Carlo simulation of Moran I
##
## data: spatial_dependency_data$SMR_2017
## weights: nb2listw(catchment_neighbours)
## number of simulations + 1: 1000
##
## statistic = 0.10287, observed rank = 844, p-value = 0.156
## alternative hypothesis: greatermoran.mc(spatial_dependency_data$SMR_2018,
nb2listw(catchment_neighbours),
nsim = 999)##
## Monte-Carlo simulation of Moran I
##
## data: spatial_dependency_data$SMR_2018
## weights: nb2listw(catchment_neighbours)
## number of simulations + 1: 1000
##
## statistic = 0.16261, observed rank = 908, p-value = 0.092
## alternative hypothesis: greatermoran.mc(spatial_dependency_data$SMR_2019,
nb2listw(catchment_neighbours),
nsim = 999)##
## Monte-Carlo simulation of Moran I
##
## data: spatial_dependency_data$SMR_2019
## weights: nb2listw(catchment_neighbours)
## number of simulations + 1: 1000
##
## statistic = 0.051131, observed rank = 773, p-value = 0.227
## alternative hypothesis: greatermoran.mc(spatial_dependency_data$SMR_2020,
nb2listw(catchment_neighbours),
nsim = 999)##
## Monte-Carlo simulation of Moran I
##
## data: spatial_dependency_data$SMR_2020
## weights: nb2listw(catchment_neighbours)
## number of simulations + 1: 1000
##
## statistic = 0.10407, observed rank = 838, p-value = 0.162
## alternative hypothesis: greater# Run a Conditional Autoregressive (CAR) model, which allows us to incorporate
# the spatial autocorrelation between neighbours within our GLM
# First, generate a weights matrix from a neighbours list with spatial weights
adj_matrix <- spdep::nb2mat(catchment_neighbours, style = "B") # see ?nb2mat
# Match row and column names with those of geographic location index
rownames(adj_matrix) <- colnames(adj_matrix) <- spatial_dependency_data$rowID
# row.names(adj_matrix) <- NULL # alternatively
# Now we can fit the model. The spatial effect is called using the adjacency function which
# requires the grouping factor (i.e. the rowID of each catchment area)
CAR_model_1km_2017 <- spaMM::fitme(observed_2017~prop_pop_1km+offset(log(expected_2017)),
adjMatrix = adj_matrix,
data = model_data_2017,
family = 'poisson')
# Generate 95% CI
coefs <- as.data.frame(summary(CAR_model_1km_2017)$beta_table)## formula: observed_2017 ~ prop_pop_1km + offset(log(expected_2017))
## Estimation of fixed effects by ML.
## family: poisson( link = log )
## ------------ Fixed effects (beta) ------------
## Estimate Cond. SE t-value
## (Intercept) -0.29002 0.006763 -42.88
## prop_pop_1km 0.03259 0.000570 57.18
## ------------- Likelihood values -------------
## logLik
## p(h) (Likelihood): -4915.088# Moran's I contiguity test
MI_2017 <- spdep::moran(model_data_2017$observed_2017,
nb2listw(catchment_neighbours),
length(model_data_2017$observed_2017),
Szero(nb2listw(catchment_neighbours)))13 Yearly variation in observed malaria cases as a risk factor
# Prep model data --------------------------------------------------------------
df2017 <- model_data_2017 |>
dplyr::as_tibble() |>
dplyr::rename(observed_cases = observed_2017,
expected_cases = expected_2017,
health_facility = Names) |>
dplyr::select(-geometry, -fid,-DN, -X, -SMR,
-pop_2017, -dr_2018, -dr_2019, -dr_2020)
df2017$year <- "2017" # add new column
# df2017 <- cbind(df2017, year = "2017") # alternatively
df2018 <- model_data_2018 |>
dplyr::as_tibble() |>
dplyr::rename(observed_cases = observed_2018,
expected_cases = expected_2018) |>
dplyr::select(-geometry, -fid,-DN, -X, -SMR,
-pop_2018,-dr_2017, -dr_2019, -dr_2020)
df2018$year <- "2018"
colnames(df2018) <- colnames(df2017) # match columns names
df2019 <- model_data_2019 |>
dplyr::as_tibble() |>
dplyr::rename(observed_cases = observed_2019,
expected_cases = expected_2019) |>
dplyr::select(-geometry, -fid,-DN, -X, -SMR,
-pop_2019, -dr_2017, -dr_2018, -dr_2020)
df2019$year <- "2019"
colnames(df2019) <- colnames(df2017) # match columns names
df2020 <- model_data_2020 |>
dplyr::as_tibble() |>
dplyr::rename(observed_cases = observed_2020,
expected_cases = expected_2020) |>
dplyr::select(-geometry, -fid,-DN, -X, -SMR,
-pop_2020, -dr_2017, -dr_2018, -dr_2019)
df2020$year <- "2020"
colnames(df2020) <- colnames(df2017) # match columns names
model_data <- rbind(df2017, df2018, df2019, df2020)
imputeTS::na.replace(model_data, 0) # replace NA with zero## # A tibble: 104 x 8
## rowID health_facility observed_cases expected_cases prop_pop_1km prop_pop_2km
## <int> <chr> <int> <dbl> <dbl> <dbl>
## 1 1 Lodjwa Health ~ 564 826 10 7
## 2 2 Nkhamenya Rura~ 2720 3344 4 16
## 3 3 Newa Mpasazi H~ 216 1156 1 7
## 4 4 Mpepa /Chising~ 1523 2287 0 0
## 5 5 Mnyanja Health~ 1480 3327 2 6
## 6 6 Simlemba Healt~ 1159 2249 2 8
## 7 7 Ofesi Health C~ 1930 2340 0 2
## 8 8 Chulu Health C~ 3482 2324 7 23
## 9 9 Kapelula Healt~ 2970 2976 2 8
## 10 10 Livwezi Health~ 594 1833 8 20
## # ... with 94 more rows, and 2 more variables: prop_pop_3km <dbl>, year <chr>model_data |> # View model data in table format
gt::gt() |>
gt::tab_style(style = list(cell_text(align = "center")),
locations = cells_column_labels() ) |>
gt::cols_label(health_facility = "Health facility",
observed_cases = "Observed cases",
expected_cases = "Expected cases",
prop_pop_1km = "Proportion of population in 1km buffers%",
prop_pop_2km = "Proportion of population in 2km buffers%",
prop_pop_3km = "Proportion of population in 3km buffers%",
year = "Year")| rowID | Health facility | Observed cases | Expected cases | Proportion of population in 1km buffers% | Proportion of population in 2km buffers% | Proportion of population in 3km buffers% | Year |
|---|---|---|---|---|---|---|---|
| 1 | Lodjwa Health Centre | 564 | 826 | 10 | 7 | 19 | 2017 |
| 2 | Nkhamenya Rural Hospital | 2720 | 3344 | 4 | 16 | 30 | 2017 |
| 3 | Newa Mpasazi Health Centre | 216 | 1156 | 1 | 7 | 14 | 2017 |
| 4 | Mpepa /Chisinga Health Centre | 1523 | 2287 | 0 | 0 | 0 | 2017 |
| 5 | Mnyanja Health Centre | 1480 | 3327 | 2 | 6 | 19 | 2017 |
| 6 | Simlemba Health Centre | 1159 | 2249 | 2 | 8 | 17 | 2017 |
| 7 | Ofesi Health Centre | 1930 | 2340 | 0 | 2 | 2 | 2017 |
| 8 | Chulu Health Centre | 3482 | 2324 | 7 | 23 | 45 | 2017 |
| 9 | Kapelula Health Centre | 2970 | 2976 | 2 | 8 | 16 | 2017 |
| 10 | Livwezi Health Centre | 594 | 1833 | 8 | 20 | 40 | 2017 |
| 11 | Gogode Dispensary | 1553 | 1088 | 6 | 19 | 39 | 2017 |
| 12 | Dwangwa Dispensary | 1153 | 2724 | 9 | 29 | 54 | 2017 |
| 13 | Chamama Health Facility | 1005 | 1668 | 2 | 9 | 16 | 2017 |
| 14 | Wimbe Health Centre | 2558 | 988 | 21 | 53 | 80 | 2017 |
| 15 | Chinyama | 1140 | 1063 | 11 | 30 | 46 | 2017 |
| 16 | Mdunga Health Centre | 1382 | 1514 | 0 | 0 | 0 | 2017 |
| 17 | Mtunthama Health Centre | 1982 | 1561 | 21 | 64 | 82 | 2017 |
| 18 | Kasungu District Hospital | 14663 | 11951 | 18 | 39 | 58 | 2017 |
| 19 | Chamwabvi Health Centre | 2031 | 2945 | 7 | 24 | 46 | 2017 |
| 20 | Linyangwa Health Centre | 1987 | 1480 | 5 | 20 | 41 | 2017 |
| 21 | Mziza Health Centre | 4098 | 3689 | 13 | 30 | 51 | 2017 |
| 22 | Kawamba Health Centre | 3845 | 2198 | 13 | 34 | 60 | 2017 |
| 23 | Kamboni Health Centre | 2588 | 1768 | 6 | 20 | 33 | 2017 |
| 24 | Khola Health Centre | 1012 | 1888 | 2 | 8 | 16 | 2017 |
| 25 | Santhe Health Centre | 5668 | 3660 | 4 | 13 | 21 | 2017 |
| 26 | Mkhota Health Centre | 1487 | 1940 | 5 | 17 | 24 | 2017 |
| 1 | Lodjwa Health Centre | 1151 | 934 | 0 | 0 | 0 | 2018 |
| 2 | Nkhamenya Rural Hospital | 3343 | 3785 | 3 | 6 | 13 | 2018 |
| 3 | Newa Mpasazi Health Centre | 434 | 1295 | 1 | 7 | 16 | 2018 |
| 4 | Mpepa /Chisinga Health Centre | 2616 | 2589 | 4 | 16 | 31 | 2018 |
| 5 | Mnyanja Health Centre | 1715 | 3804 | 3 | 11 | 30 | 2018 |
| 6 | Simlemba Health Centre | 1506 | 2496 | 3 | 10 | 23 | 2018 |
| 7 | Ofesi Health Centre | 1773 | 2636 | 2 | 5 | 10 | 2018 |
| 8 | Chulu Health Centre | 3330 | 2621 | 11 | 39 | 65 | 2018 |
| 9 | Kapelula Health Centre | 3480 | 3420 | 9 | 27 | 51 | 2018 |
| 10 | Livwezi Health Centre | 1128 | 2049 | 8 | 25 | 54 | 2018 |
| 11 | Gogode Dispensary | 2550 | 1215 | 9 | 22 | 44 | 2018 |
| 12 | Dwangwa Dispensary | 1216 | 3048 | 10 | 37 | 61 | 2018 |
| 13 | Chamama Health Facility | 1226 | 1852 | 3 | 12 | 22 | 2018 |
| 14 | Wimbe Health Centre | 3167 | 1074 | 20 | 54 | 80 | 2018 |
| 15 | Chinyama | 1673 | 1194 | 14 | 33 | 48 | 2018 |
| 16 | Mdunga Health Centre | 1894 | 1720 | 7 | 17 | 45 | 2018 |
| 17 | Mtunthama Health Centre | 3358 | 1734 | 25 | 69 | 84 | 2018 |
| 18 | Kasungu District Hospital | 12019 | 13377 | 19 | 50 | 66 | 2018 |
| 19 | Chamwabvi Health Centre | 2079 | 3287 | 16 | 46 | 70 | 2018 |
| 20 | Linyangwa Health Centre | 1500 | 1639 | 5 | 18 | 39 | 2018 |
| 21 | Mziza Health Centre | 2291 | 4210 | 17 | 40 | 58 | 2018 |
| 22 | Kawamba Health Centre | 3881 | 2386 | 33 | 62 | 84 | 2018 |
| 23 | Kamboni Health Centre | 3250 | 1948 | 19 | 36 | 53 | 2018 |
| 24 | Khola Health Centre | 1697 | 2108 | 5 | 14 | 28 | 2018 |
| 25 | Santhe Health Centre | 6195 | 4096 | 6 | 21 | 36 | 2018 |
| 26 | Mkhota Health Centre | 4218 | 2171 | 17 | 40 | 66 | 2018 |
| 1 | Lodjwa Health Centre | 1168 | 909 | 10 | 3 | 10 | 2019 |
| 2 | Nkhamenya Rural Hospital | 3932 | 3709 | 10 | 29 | 58 | 2019 |
| 3 | Newa Mpasazi Health Centre | 626 | 1266 | 5 | 18 | 44 | 2019 |
| 4 | Mpepa /Chisinga Health Centre | 4169 | 2523 | 3 | 11 | 19 | 2019 |
| 5 | Mnyanja Health Centre | 2504 | 3751 | 7 | 17 | 36 | 2019 |
| 6 | Simlemba Health Centre | 1788 | 2405 | 11 | 32 | 55 | 2019 |
| 7 | Ofesi Health Centre | 2124 | 2576 | 5 | 20 | 36 | 2019 |
| 8 | Chulu Health Centre | 3537 | 2547 | 27 | 60 | 86 | 2019 |
| 9 | Kapelula Health Centre | 3357 | 3405 | 10 | 35 | 54 | 2019 |
| 10 | Livwezi Health Centre | 435 | 1966 | 17 | 43 | 72 | 2019 |
| 11 | Gogode Dispensary | 1469 | 1169 | 10 | 22 | 42 | 2019 |
| 12 | Dwangwa Dispensary | 1370 | 2948 | 22 | 64 | 85 | 2019 |
| 13 | Chamama Health Facility | 1127 | 1773 | 2 | 13 | 18 | 2019 |
| 14 | Wimbe Health Centre | 2162 | 1016 | 21 | 54 | 81 | 2019 |
| 15 | Chinyama | 1260 | 1154 | 14 | 35 | 48 | 2019 |
| 16 | Mdunga Health Centre | 1485 | 1710 | 8 | 33 | 50 | 2019 |
| 17 | Mtunthama Health Centre | 1718 | 1661 | 30 | 76 | 91 | 2019 |
| 18 | Kasungu District Hospital | 13052 | 12942 | 25 | 58 | 90 | 2019 |
| 19 | Chamwabvi Health Centre | 1180 | 3161 | 13 | 54 | 77 | 2019 |
| 20 | Linyangwa Health Centre | 2692 | 1566 | 15 | 45 | 77 | 2019 |
| 21 | Mziza Health Centre | 3135 | 4151 | 28 | 66 | 89 | 2019 |
| 22 | Kawamba Health Centre | 3469 | 2258 | 42 | 78 | 94 | 2019 |
| 23 | Kamboni Health Centre | 2537 | 1843 | 28 | 52 | 78 | 2019 |
| 24 | Khola Health Centre | 2139 | 2040 | 10 | 19 | 34 | 2019 |
| 25 | Santhe Health Centre | 5793 | 3957 | 24 | 58 | 81 | 2019 |
| 26 | Mkhota Health Centre | 2268 | 2093 | 23 | 53 | 86 | 2019 |
| 1 | Lodjwa Health Centre | 1788 | 1538 | 1 | 4 | 9 | 2020 |
| 2 | Nkhamenya Rural Hospital | 8539 | 6313 | 5 | 20 | 42 | 2020 |
| 3 | Newa Mpasazi Health Centre | 2182 | 2153 | 2 | 8 | 17 | 2020 |
| 4 | Mpepa /Chisinga Health Centre | 5186 | 4270 | 0 | 0 | 0 | 2020 |
| 5 | Mnyanja Health Centre | 6117 | 6426 | 1 | 2 | 5 | 2020 |
| 6 | Simlemba Health Centre | 5310 | 4026 | 3 | 10 | 22 | 2020 |
| 7 | Ofesi Health Centre | 2323 | 4379 | 0 | 3 | 2 | 2020 |
| 8 | Chulu Health Centre | 7160 | 4308 | 13 | 36 | 54 | 2020 |
| 9 | Kapelula Health Centre | 7297 | 5904 | 0 | 0 | 0 | 2020 |
| 10 | Livwezi Health Centre | 1028 | 3267 | 7 | 17 | 38 | 2020 |
| 11 | Gogode Dispensary | 2767 | 1961 | 7 | 20 | 40 | 2020 |
| 12 | Dwangwa Dispensary | 2869 | 4971 | 9 | 34 | 58 | 2020 |
| 13 | Chamama Health Facility | 635 | 2969 | 2 | 8 | 14 | 2020 |
| 14 | Wimbe Health Centre | 2233 | 1689 | 21 | 53 | 80 | 2020 |
| 15 | Chinyama | 1605 | 1936 | 11 | 28 | 45 | 2020 |
| 16 | Mdunga Health Centre | 3169 | 2952 | 0 | 0 | 0 | 2020 |
| 17 | Mtunthama Health Centre | 1882 | 2763 | 20 | 65 | 82 | 2020 |
| 18 | Kasungu District Hospital | 19393 | 21785 | 19 | 40 | 62 | 2020 |
| 19 | Chamwabvi Health Centre | 1128 | 5304 | 9 | 28 | 53 | 2020 |
| 20 | Linyangwa Health Centre | 4380 | 2604 | 5 | 20 | 42 | 2020 |
| 21 | Mziza Health Centre | 5791 | 7131 | 18 | 29 | 45 | 2020 |
| 22 | Kawamba Health Centre | 7073 | 3771 | 19 | 42 | 64 | 2020 |
| 23 | Kamboni Health Centre | 4665 | 3028 | 8 | 28 | 43 | 2020 |
| 24 | Khola Health Centre | 3426 | 3453 | 4 | 13 | 21 | 2020 |
| 25 | Santhe Health Centre | 6556 | 6667 | 5 | 17 | 31 | 2020 |
| 26 | Mkhota Health Centre | 4592 | 3526 | 10 | 30 | 55 | 2020 |
# Model fitting ----------------------------------------------------------------
multivariate_1km <- glm(observed_cases~1+prop_pop_1km+year+offset(log(expected_cases)),
data = model_data, family = 'poisson')
summary(multivariate_1km)##
## Call:
## glm(formula = observed_cases ~ 1 + prop_pop_1km + year + offset(log(expected_cases)),
## family = "poisson", data = model_data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -69.294 -16.295 3.269 14.341 47.265
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1107219 0.0043691 -25.342 <2e-16 ***
## prop_pop_1km 0.0131038 0.0002185 59.964 <2e-16 ***
## year2018 -0.0457653 0.0054605 -8.381 <2e-16 ***
## year2019 -0.1309678 0.0058898 -22.236 <2e-16 ***
## year2020 -0.0141889 0.0048881 -2.903 0.0037 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 60683 on 103 degrees of freedom
## Residual deviance: 57121 on 99 degrees of freedom
## AIC: 58129
##
## Number of Fisher Scoring iterations: 4# Build regression model table
regression_table_1km <- gtsummary::tbl_regression(
multivariate_1km, exponentiate = FALSE) |>
gtsummary::bold_p()
sjPlot::tab_model(multivariate_1km, show.r2 = FALSE, show.aic = TRUE)| observed_cases | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.90 | 0.89 – 0.90 | <0.001 |
| prop_pop_1km | 1.01 | 1.01 – 1.01 | <0.001 |
| year [2018] | 0.96 | 0.95 – 0.97 | <0.001 |
| year [2019] | 0.88 | 0.87 – 0.89 | <0.001 |
| year [2020] | 0.99 | 0.98 – 1.00 | 0.004 |
| Observations | 104 | ||
| AIC | 58129.020 | ||
multivariate_2km <- glm(observed_cases~1+prop_pop_2km+year+offset(log(expected_cases)),
data = model_data, family = 'poisson')
summary(multivariate_2km)##
## Call:
## glm(formula = observed_cases ~ 1 + prop_pop_2km + year + offset(log(expected_cases)),
## family = "poisson", data = model_data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -70.975 -16.845 2.141 14.947 46.898
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1141736 0.0045738 -24.962 <2e-16 ***
## prop_pop_2km 0.0051303 0.0001025 50.030 <2e-16 ***
## year2018 -0.0485054 0.0054937 -8.829 <2e-16 ***
## year2019 -0.1194495 0.0059579 -20.049 <2e-16 ***
## year2020 -0.0084713 0.0048849 -1.734 0.0829 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 60683 on 103 degrees of freedom
## Residual deviance: 58179 on 99 degrees of freedom
## AIC: 59187
##
## Number of Fisher Scoring iterations: 4regression_table_2km <- gtsummary::tbl_regression(
multivariate_2km)|>
gtsummary::bold_p()
sjPlot::tab_model(multivariate_2km, show.r2 = FALSE, show.aic = TRUE)| observed_cases | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.89 | 0.88 – 0.90 | <0.001 |
| prop_pop_2km | 1.01 | 1.00 – 1.01 | <0.001 |
| year [2018] | 0.95 | 0.94 – 0.96 | <0.001 |
| year [2019] | 0.89 | 0.88 – 0.90 | <0.001 |
| year [2020] | 0.99 | 0.98 – 1.00 | 0.083 |
| Observations | 104 | ||
| AIC | 59186.646 | ||
multivariate_3km <- glm(observed_cases~1+prop_pop_3km+year+offset(log(expected_cases)),
data = model_data, family = 'poisson')
summary(multivariate_3km)##
## Call:
## glm(formula = observed_cases ~ 1 + prop_pop_3km + year + offset(log(expected_cases)),
## family = "poisson", data = model_data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -72.685 -17.393 1.887 15.771 47.060
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.338e-01 4.922e-03 -27.174 < 2e-16 ***
## prop_pop_3km 3.599e-03 7.809e-05 46.084 < 2e-16 ***
## year2018 -4.363e-02 5.485e-03 -7.954 1.8e-15 ***
## year2019 -1.130e-01 5.975e-03 -18.918 < 2e-16 ***
## year2020 -7.825e-03 4.885e-03 -1.602 0.109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 60683 on 103 degrees of freedom
## Residual deviance: 58533 on 99 degrees of freedom
## AIC: 59540
##
## Number of Fisher Scoring iterations: 4regression_table_3km <- gtsummary::tbl_regression(
multivariate_3km, exponentiate = FALSE) |>
gtsummary::bold_p()
sjPlot::tab_model(multivariate_3km, show.r2 = FALSE, show.aic = TRUE)| observed_cases | |||
|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | p |
| (Intercept) | 0.87 | 0.87 – 0.88 | <0.001 |
| prop_pop_3km | 1.00 | 1.00 – 1.00 | <0.001 |
| year [2018] | 0.96 | 0.95 – 0.97 | <0.001 |
| year [2019] | 0.89 | 0.88 – 0.90 | <0.001 |
| year [2020] | 0.99 | 0.98 – 1.00 | 0.109 |
| Observations | 104 | ||
| AIC | 59540.390 | ||
# merge regression model tables
table_merge <-gtsummary::tbl_merge(
tbls = list(regression_table_1km,
regression_table_2km,
regression_table_3km),
tab_spanner = c("**Model 1km**",
"**Model 2km**",
"**Model 3km**"))
table_merge| Characteristic | Model 1km | Model 2km | Model 3km | ||||||
|---|---|---|---|---|---|---|---|---|---|
| log(IRR)1 | 95% CI1 | p-value | log(IRR)1 | 95% CI1 | p-value | log(IRR)1 | 95% CI1 | p-value | |
| prop_pop_1km | 0.01 | 0.01, 0.01 | <0.001 | ||||||
| year | |||||||||
| 2017 | — | — | — | — | — | — | |||
| 2018 | -0.05 | -0.06, -0.04 | <0.001 | -0.05 | -0.06, -0.04 | <0.001 | -0.04 | -0.05, -0.03 | <0.001 |
| 2019 | -0.13 | -0.14, -0.12 | <0.001 | -0.12 | -0.13, -0.11 | <0.001 | -0.11 | -0.12, -0.10 | <0.001 |
| 2020 | -0.01 | -0.02, 0.00 | 0.004 | -0.01 | -0.02, 0.00 | 0.083 | -0.01 | -0.02, 0.00 | 0.11 |
| prop_pop_2km | 0.01 | 0.00, 0.01 | <0.001 | ||||||
| prop_pop_3km | 0.00 | 0.00, 0.00 | <0.001 | ||||||
|
1
IRR = Incidence Rate Ratio, CI = Confidence Interval
|
|||||||||
# Check model perfomance -------------------------------------------------------
performance::check_model(multivariate_1km, theme = "see::theme_modern")
performance::check_model(multivariate_2km, theme = "see::theme_modern")
performance::check_model(multivariate_3km, theme = "see::theme_modern")
performance::compare_performance(
multivariate_1km, multivariate_2km, multivariate_3km,
metrics = c("AIC", "BIC", "RMSE", "Sigma"), rank = TRUE)## # Comparison of Model Performance Indices
##
## Name | Model | AIC | BIC | RMSE | Sigma | Performance-Score
## ----------------------------------------------------------------------------------------
## multivariate_1km | glm | 58129.020 | 58142.242 | 1372.801 | 24.020 | 75.00%
## multivariate_2km | glm | 59186.646 | 59199.868 | 1352.076 | 24.242 | 43.44%
## multivariate_3km | glm | 59540.390 | 59553.611 | 1351.802 | 24.315 | 25.00%13.1 Estimate prediction error
Since our dataset is small and partitioning it will lead to higher bias. Therefore, we use the Leave one out cross validation (LOOCV) method which uses all data points to measure performance of the Poisson model. This method works as follow:
- Leave out one data point and build the model on the rest of the data set
- Test the model against the data point that is left out at step 1 and record the test error associated with the prediction
- Repeat the process for all data points
- Compute the overall prediction error by taking the average of all these test error estimates recorded at step 2.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2014. An Introduction to Statistical Learning: With Applications in R. Springer Publishing Company, Incorporated.
# Define training control
train_control <- caret::trainControl(method = "LOOCV")
# Removing missing values
model_data_evaluation <- model_data[complete.cases(model_data),]
# Train the model, fit a regression model and use LOOCV to evaluate perfomance
train_model_1km <- caret::train(
observed_cases~1+prop_pop_1km+year+offset(log(expected_cases)),
data = model_data_evaluation, method = "glm",
trControl = train_control, family = "poisson")
train_model_2km <- caret::train(
observed_cases~1+prop_pop_2km+year+offset(log(expected_cases)),
data = model_data_evaluation, method = "glm",
trControl = train_control, family = "poisson")
train_model_3km <- caret::train(
observed_cases~1+prop_pop_3km+year+offset(log(expected_cases)),
data = model_data_evaluation, method = "glm",
trControl = train_control, family = "poisson")
# Summarize the results
# The lower the Mean Absolute Error, the more closely a model can predict the actual observations
print(train_model_1km)## Generalized Linear Model
##
## 104 samples
## 3 predictor
##
## No pre-processing
## Resampling: Leave-One-Out Cross-Validation
## Summary of sample sizes: 103, 103, 103, 103, 103, 103, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 2854.871 0.07951775 1774.594summary(train_model_1km)##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -70.91 -25.76 -11.04 10.69 138.63
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 7.5596403 0.0042883 1762.873 < 2e-16 ***
## prop_pop_1km 0.0345465 0.0002076 166.381 < 2e-16 ***
## year2018 -0.0241893 0.0054887 -4.407 1.05e-05 ***
## year2019 -0.2740150 0.0059470 -46.076 < 2e-16 ***
## year2020 0.5769079 0.0048867 118.056 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 201942 on 103 degrees of freedom
## Residual deviance: 154281 on 99 degrees of freedom
## AIC: 155289
##
## Number of Fisher Scoring iterations: 5print(train_model_2km)## Generalized Linear Model
##
## 104 samples
## 3 predictor
##
## No pre-processing
## Resampling: Leave-One-Out Cross-Validation
## Summary of sample sizes: 103, 103, 103, 103, 103, 103, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 2920.994 0.04763413 1780.845summary(train_model_2km)##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -80.94 -26.36 -11.11 11.99 148.88
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 7.549e+00 4.478e-03 1685.834 <2e-16 ***
## prop_pop_2km 1.288e-02 9.391e-05 137.173 <2e-16 ***
## year2018 3.048e-03 5.472e-03 0.557 0.578
## year2019 -1.985e-01 5.853e-03 -33.913 <2e-16 ***
## year2020 5.813e-01 4.887e-03 118.952 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 201942 on 103 degrees of freedom
## Residual deviance: 162246 on 99 degrees of freedom
## AIC: 163254
##
## Number of Fisher Scoring iterations: 5print(train_model_3km)## Generalized Linear Model
##
## 104 samples
## 3 predictor
##
## No pre-processing
## Resampling: Leave-One-Out Cross-Validation
## Summary of sample sizes: 103, 103, 103, 103, 103, 103, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 2904.301 0.05092564 1766.576summary(train_model_3km)##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -73.324 -25.390 -11.284 9.963 149.735
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 7.464e+00 4.870e-03 1532.691 <2e-16 ***
## prop_pop_3km 9.929e-03 7.484e-05 132.678 <2e-16 ***
## year2018 -3.717e-03 5.477e-03 -0.679 0.497
## year2019 -1.965e-01 5.848e-03 -33.596 <2e-16 ***
## year2020 5.834e-01 4.887e-03 119.391 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 201942 on 103 degrees of freedom
## Residual deviance: 162692 on 99 degrees of freedom
## AIC: 163699
##
## Number of Fisher Scoring iterations: 5Model-based Geostatistics
# Run a Kriging model ----------------------------------------------------------
# Get point location of health facilities and convert to spatial object
zipatala <-zipatala_aggregated_sf |>
sf::st_transform(32736) # reproject
zipatala_sp <- as(zipatala, "Spatial")
# Make a vector that is TRUE for the missing dry season malaria data
# 2017
missing_2017 <- is.na(zipatala_sp$dr_2017)
table(missing_2017) # no missing data## missing_2017
## FALSE
## 26# Plot a map of dry season malaria cases
# raster::spplot(zipatala_sp, zcol = "dr_2017")
# Kriging with linear trend, predicting over the missing points
malaria_autoKrige_2017 <- automap::autoKrige(
dr_2017 ~ coords.x1 + coords.x2,
input_data = zipatala_sp[!missing_2017, ],
new_data = zipatala_sp,
model = "Mat") # Matern## [using universal kriging]# Plot the variogram, predictions, and standard error
plot(malaria_autoKrige_2017)
# Function to split an scf_POINT object with single geometry column to
# columns with separate longitude and latitude ---------------------------------
separate.coordinates <- function(x, names = c("long","lat")) {
stopifnot(inherits(x,"sf") && inherits(sf::st_geometry(x), "sfc_POINT"))
ret <- do.call(rbind, sf::st_geometry(x))
ret <- tibble::as_tibble(ret)
stopifnot(length(names) == ncol(ret))
ret <- setNames(ret, names)
dplyr::bind_cols(x, ret)
}
catchment_points <- separate.coordinates(zipatala_aggregated_sf) |>
dplyr::select(rowID, long, lat)
variogram_data <- merge(as.data.frame(catchment_points),
as.data.frame(model_data_2017),
by = "rowID") |>
dplyr::select(long, lat, prop_pop_1km, prop_pop_2km, prop_pop_3km,
observed_2017, expected_2017)
# write.csv(variogram_data, "data/variogram_data.csv")
# Open the MBG app in R. Type the following into the R console:
# shiny::runGitHub(repo="MBGapp", username= "olatunjijohnson",
# ref="main", subdir = "inst/MBGapp")